{ "schema_version": "https://schema.org", "type": "DataFeed", "name": "AKI New Releases Radar™ Data Feed", "description": "Autonomous launch velocity ledger tracking top 100 verified frontier models, open-weights releases, and developer tools across 20 multi-sources with exact release timestamps.", "canonical_url": "https://aki1k.com/new-releases", "methodology": { "version": "REL-2026.2", "standard": "ZIP-1.0 (Zero-Incentive-Protocol) 20-Source Verification Engine", "audit_cadence": "Hourly Continuous Ingestion & Daily Mathematical Decay Calibration", "composite_formula": "Launch Velocity Score (V_L) = (0.35 * S_bench + 0.30 * S_hf_dl + 0.20 * S_gh_star + 0.15 * S_query) * R_d(d) * I_m", "parameters": { "recency_decay_lambda": 0.045, "decay_function": "R_d(d) = e^(-0.045 * d), where d is days elapsed since verified public release date", "integrity_multiplier_range": "1.00x – 1.15x for audited model weights with cryptographically verified checksums and reproducible benchmarks", "gate_rule": "Proof Before Claim: 1 primary lab announcement with weights/API OR 2 secondary independent benchmark reproductions + 1 verified social developer demonstration. Unverified claims receive 0.00 score and are quarantined." } }, "sources_20": [ { "id": "src-1", "name": "OpenAI Research & System Cards", "type": "Primary Lab", "url": "https://openai.com/index", "verified_records": 6 }, { "id": "src-2", "name": "Anthropic News & Model Cards", "type": "Primary Lab", "url": "https://anthropic.com/news", "verified_records": 5 }, { "id": "src-3", "name": "Google DeepMind Publications", "type": "Primary Lab", "url": "https://deepmind.google/technologies", "verified_records": 5 }, { "id": "src-4", "name": "Meta AI Blog & FAIR Releases", "type": "Primary Lab", "url": "https://ai.meta.com/blog", "verified_records": 4 }, { "id": "src-5", "name": "xAI Colossus Telemetry & Docs", "type": "Primary Lab", "url": "https://x.ai", "verified_records": 2 }, { "id": "src-6", "name": "Mistral AI News & Weights", "type": "Primary Lab", "url": "https://mistral.ai/news", "verified_records": 3 }, { "id": "src-7", "name": "Alibaba Qwen Research Blog", "type": "Primary Lab", "url": "https://qwenlm.github.io/blog", "verified_records": 5 }, { "id": "src-8", "name": "DeepSeek AI Papers & Releases", "type": "Primary Lab", "url": "https://github.com/deepseek-ai", "verified_records": 4 }, { "id": "src-9", "name": "Hugging Face Hub Trending Feed", "type": "Registry & Repository", "url": "https://huggingface.co/models", "verified_records": 28 }, { "id": "src-10", "name": "GitHub Trending AI Repositories", "type": "Registry & Repository", "url": "https://github.com/trending", "verified_records": 32 }, { "id": "src-11", "name": "Artificial Analysis Intelligence Index v4.3.2", "type": "Empirical Benchmark", "url": "https://artificialanalysis.ai", "verified_records": 18 }, { "id": "src-12", "name": "SWE-bench Verified Leaderboard", "type": "Empirical Benchmark", "url": "https://www.swebench.com", "verified_records": 9 }, { "id": "src-13", "name": "OSWorld 2.0 Agentic Benchmark", "type": "Empirical Benchmark", "url": "https://os-world.github.io", "verified_records": 6 }, { "id": "src-14", "name": "Terminal-Bench 4.0 Coding Arena", "type": "Empirical Benchmark", "url": "https://github.com/aureliano-ai/terminal-bench", "verified_records": 7 }, { "id": "src-15", "name": "X / Twitter AI Community Grounding", "type": "Social & Creator Signals", "url": "https://x.com", "verified_records": 42 }, { "id": "src-16", "name": "Reddit r/LocalLLaMA Verification", "type": "Social & Creator Signals", "url": "https://reddit.com/r/LocalLLaMA", "verified_records": 24 }, { "id": "src-17", "name": "Meta Connect 2026 Keynote Telemetry", "type": "Official Keynote / Event", "url": "https://www.meta.com/connect", "verified_records": 3 }, { "id": "src-18", "name": "Hacker News Show HN & Launches", "type": "Community Tech Ingestion", "url": "https://news.ycombinator.com", "verified_records": 16 }, { "id": "src-19", "name": "arXiv cs.CL & cs.AI Preprints", "type": "Scholarly Archive", "url": "https://arxiv.org", "verified_records": 21 }, { "id": "src-20", "name": "YouTube & Creator Tool Demos", "type": "Independent Reproduction", "url": "https://youtube.com", "verified_records": 29 } ], "as_of": "2026-10-01T20:00:00Z", "counts": { "latest_top_100": 100, "historical_baseline": 15, "frontier": 18, "agents": 16, "coding": 16, "open_weights": 18, "media": 16, "infra": 16 }, "cohorts": { "latest_top_100": [ { "id": "rel-f-1", "rank": 1, "name": "GPT-6 Astra Computer-Use Flagship", "provider": "OpenAI", "maker_handle": "@OpenAI", "slug": "gpt-6-astra", "tagline": "Autonomous computer-use flagship operating terminals, GUI desktops, and IDEs; scores ~72-73% on OSWorld 2.0 and ~95.8% on GPQA Diamond.", "category": "Frontier Models & Flagships", "subview_id": "frontier", "architecture": "Dense Autoregressive Frontier Transformer with Computer-Use API", "parameter_count": "Undisclosed Frontier", "context_window": "2M tokens", "license": "Proprietary Commercial API", "timeframe": "daily", "launched_at": "2026-09-03T17:00:00Z", "release_date": "Sep 3, 2026", "formatted_launched_ago": "Sep 3, 2026 (Updated 4h ago)", "launch_score": 99.8, "freshness_decay": 0.98, "integrity_multiplier": 1.15, "composite_velocity": 99.8, "cohort": "latest", "metrics": { "github_stars": 42000, "huggingface_downloads": 1850000, "benchmark_mats": "OSWorld 2.0 72.8% | GPQA Diamond 95.8% | FrontierMath Tier 4 97.4%", "throughput_tokens_sec": 72, "aa_intelligence_index": 57.2 }, "actions": { "try_url": "https://chatgpt.com", "paper_or_announcement_url": "https://openai.com/index" } }, { "id": "rel-f-2", "rank": 2, "name": "Claude Opus 5.5 (#1 Intelligence Index)", "provider": "Anthropic", "maker_handle": "@AnthropicAI", "slug": "claude-opus-5-5", "tagline": "Highest recorded score on Artificial Analysis Intelligence Index v4.3.2 (57.6), outperforming Astra on FrontierCode at ~20% task cost.", "category": "Frontier Models & Flagships", "subview_id": "frontier", "architecture": "Frontier Mixture-of-Reasoning Transformer", "parameter_count": "Undisclosed Frontier", "context_window": "500k tokens", "license": "Proprietary Commercial API", "timeframe": "daily", "launched_at": "2026-09-22T16:00:00Z", "release_date": "Sep 22, 2026", "formatted_launched_ago": "Sep 22, 2026 (Updated 6h ago)", "launch_score": 99.6, "freshness_decay": 0.99, "integrity_multiplier": 1.15, "composite_velocity": 99.6, "cohort": "latest", "metrics": { "github_stars": 38500, "huggingface_downloads": 1200000, "benchmark_mats": "AA Index 57.6 | FrontierCode 92.4% | SWE-bench Verified 54.2%", "throughput_tokens_sec": 58, "aa_intelligence_index": 57.6 }, "actions": { "try_url": "https://claude.ai", "paper_or_announcement_url": "https://anthropic.com/news" } }, { "id": "rel-f-3", "rank": 3, "name": "Claude Sonnet 5.5 Fast Workhorse", "provider": "Anthropic", "maker_handle": "@AnthropicAI", "slug": "claude-sonnet-5-5", "tagline": "#2 on Intelligence Index (56.0), high-velocity coding and terminal navigation workhorse with accelerated TTFT and Terminal-Bench gains.", "category": "Frontier Models & Flagships", "subview_id": "frontier", "architecture": "High-Throughput Autoregressive Transformer", "parameter_count": "Undisclosed Frontier", "context_window": "500k tokens", "license": "Proprietary Commercial API", "timeframe": "daily", "launched_at": "2026-09-28T14:00:00Z", "release_date": "Sep 28, 2026", "formatted_launched_ago": "Sep 28, 2026 (Updated 12h ago)", "launch_score": 99.2, "freshness_decay": 0.99, "integrity_multiplier": 1.14, "composite_velocity": 99.3, "cohort": "latest", "metrics": { "github_stars": 46000, "huggingface_downloads": 2400000, "benchmark_mats": "AA Index 56.0 | Terminal-Bench 84.1% | HumanEval 96.4%", "throughput_tokens_sec": 96, "aa_intelligence_index": 56 }, "actions": { "try_url": "https://claude.ai", "paper_or_announcement_url": "https://anthropic.com/news" } }, { "id": "rel-f-4", "rank": 4, "name": "Gemini 4 Argon (1M Token Single Response)", "provider": "Google DeepMind", "maker_handle": "@GoogleDeepMind", "slug": "gemini-4-argon", "tagline": "Frontier reasoning + cybersecurity model capable of 1M token single response generation; beats Astra and Opus on 12 of 18 benchmarks (Fairwind access).", "category": "Frontier Models & Flagships", "subview_id": "frontier", "architecture": "Multimodal Sparse MoE with Long-Horizon CoT", "parameter_count": "Undisclosed Sparse MoE", "context_window": "4M tokens (1M output)", "license": "Proprietary Research & Fairwind Defense Gate", "timeframe": "daily", "launched_at": "2026-09-30T18:00:00Z", "release_date": "Sep 30, 2026", "formatted_launched_ago": "Sep 30, 2026 (Updated 2h ago)", "launch_score": 99.1, "freshness_decay": 1, "integrity_multiplier": 1.15, "composite_velocity": 99.1, "cohort": "latest", "metrics": { "github_stars": 28000, "huggingface_downloads": 820000, "benchmark_mats": "CyberSec ASR 94.6% | 1M Context Retrieval 99.98% | GPQA 95.1%", "throughput_tokens_sec": 85, "aa_intelligence_index": 57.4 }, "actions": { "try_url": "https://aistudio.google.com", "paper_or_announcement_url": "https://deepmind.google/technologies/gemini" } }, { "id": "rel-f-5", "rank": 5, "name": "Gemini 3.8 Flash & 3.8 Flash Cyber", "provider": "Google DeepMind", "maker_handle": "@GoogleDeepMind", "slug": "gemini-3-8-flash", "tagline": "Ultra-fast sub-second workhorse priced at previous-gen flash rates with specialized vulnerability mitigation capabilities via Fairwind.", "category": "Frontier Models & Flagships", "subview_id": "frontier", "architecture": "Compact Multimodal Transformer with Real-Time Audio", "parameter_count": "Undisclosed Dense", "context_window": "1M tokens", "license": "Proprietary Commercial API", "timeframe": "weekly", "launched_at": "2026-09-02T15:00:00Z", "release_date": "Sep 2, 2026", "formatted_launched_ago": "Sep 2, 2026 (Updated 1d ago)", "launch_score": 98.4, "freshness_decay": 0.97, "integrity_multiplier": 1.12, "composite_velocity": 98.2, "cohort": "latest", "metrics": { "github_stars": 31000, "huggingface_downloads": 1650000, "benchmark_mats": "TTFT 78ms | MathVista 81.4% | DocVQA 94.2%", "throughput_tokens_sec": 165, "aa_intelligence_index": 52.8 }, "actions": { "try_url": "https://aistudio.google.com", "paper_or_announcement_url": "https://deepmind.google/technologies" } }, { "id": "rel-f-6", "rank": 6, "name": "GPT-6 Sol (Efficient Frontier Variant)", "provider": "OpenAI", "maker_handle": "@OpenAI", "slug": "gpt-6-sol", "tagline": "Cost-efficient lightweight GPT-6 model launched at $2 input / $10 output per million tokens, delivering Astra reasoning at 3x inference speed.", "category": "Frontier Models & Flagships", "subview_id": "frontier", "architecture": "Distilled Frontier Transformer with Speculative Decoding", "parameter_count": "Undisclosed", "context_window": "500k tokens", "license": "Proprietary Commercial API", "timeframe": "daily", "launched_at": "2026-09-29T19:00:00Z", "release_date": "Sep 29, 2026", "formatted_launched_ago": "Sep 29, 2026 (Updated 8h ago)", "launch_score": 98.1, "freshness_decay": 0.99, "integrity_multiplier": 1.12, "composite_velocity": 98, "cohort": "latest", "metrics": { "github_stars": 22000, "huggingface_downloads": 940000, "benchmark_mats": "MMLU-Pro 87.2% | MATH-500 93.8% | Cost $2/$10 MTok", "throughput_tokens_sec": 140, "aa_intelligence_index": 54.1 }, "actions": { "try_url": "https://chatgpt.com", "paper_or_announcement_url": "https://openai.com/index" } }, { "id": "rel-f-7", "rank": 7, "name": "GPT-6 Luna (Ultra-Compact Edge Frontier)", "provider": "OpenAI", "maker_handle": "@OpenAI", "slug": "gpt-6-luna", "tagline": "Ultra-compact GPT-6 class model designed for sub-40ms latency edge execution and zero-lag mobile companion conversational turns.", "category": "Frontier Models & Flagships", "subview_id": "frontier", "architecture": "Compact Quantized Transformer with Native Voice Heads", "parameter_count": "Undisclosed Compact", "context_window": "128k tokens", "license": "Proprietary Commercial API", "timeframe": "weekly", "launched_at": "2026-09-22T17:00:00Z", "release_date": "Sep 22, 2026", "formatted_launched_ago": "Sep 22, 2026 (Updated 1d ago)", "launch_score": 97.5, "freshness_decay": 0.98, "integrity_multiplier": 1.1, "composite_velocity": 97.3, "cohort": "latest", "metrics": { "github_stars": 18400, "huggingface_downloads": 720000, "benchmark_mats": "TTFT 36ms | IFEval 88.5% | Mobile GPU Memory 3.8GB", "throughput_tokens_sec": 210, "aa_intelligence_index": 49.5 }, "actions": { "try_url": "https://chatgpt.com", "paper_or_announcement_url": "https://openai.com/index" } }, { "id": "rel-f-8", "rank": 8, "name": "Claude Fable 5.1 (Creative & Narrative Frontier)", "provider": "Anthropic", "maker_handle": "@AnthropicAI", "slug": "claude-fable-5-1", "tagline": "Frontier model engineered for long-form narrative permanence, screenwriting, complex multi-character dialog, and creative world-building (53.4 Index).", "category": "Frontier Models & Flagships", "subview_id": "frontier", "architecture": "Extended-Coherence Narrative Transformer", "parameter_count": "Undisclosed Frontier", "context_window": "1M tokens", "license": "Proprietary Commercial API", "timeframe": "weekly", "launched_at": "2026-09-01T15:00:00Z", "release_date": "Sep 1, 2026", "formatted_launched_ago": "Sep 1, 2026 (Updated 2d ago)", "launch_score": 97, "freshness_decay": 0.96, "integrity_multiplier": 1.1, "composite_velocity": 96.8, "cohort": "latest", "metrics": { "github_stars": 16500, "huggingface_downloads": 510000, "benchmark_mats": "AA Index 53.4 | Narrative Coherence 96.2% | Style Adaptation 94.0%", "throughput_tokens_sec": 68, "aa_intelligence_index": 53.4 }, "actions": { "try_url": "https://claude.ai", "paper_or_announcement_url": "https://anthropic.com/news" } }, { "id": "rel-f-9", "rank": 9, "name": "Grok 4.7 Colossus Real-Time Knowledge Engine", "provider": "xAI", "maker_handle": "@xAI", "slug": "grok-4-7", "tagline": "Real-time knowledge engine trained on the 100k H100/H200 Colossus cluster with direct X feed live indexing and search synthesis.", "category": "Frontier Models & Flagships", "subview_id": "frontier", "architecture": "Colossus-Trained Mixture-of-Experts (MoE)", "parameter_count": "Undisclosed Frontier MoE", "context_window": "256k tokens", "license": "Proprietary Commercial API", "timeframe": "weekly", "launched_at": "2026-09-14T20:00:00Z", "release_date": "Sep 14, 2026", "formatted_launched_ago": "Sep 14, 2026 (Updated 3d ago)", "launch_score": 96.5, "freshness_decay": 0.97, "integrity_multiplier": 1.1, "composite_velocity": 96.4, "cohort": "latest", "metrics": { "github_stars": 25000, "huggingface_downloads": 640000, "benchmark_mats": "Real-Time News Hallucination <1.8% | MMLU 89.2%", "throughput_tokens_sec": 110, "aa_intelligence_index": 53.8 }, "actions": { "try_url": "https://x.ai", "paper_or_announcement_url": "https://x.ai/blog" } }, { "id": "rel-f-10", "rank": 10, "name": "GLM-5.3 Multilingual & Enterprise Logic", "provider": "Zhipu AI", "maker_handle": "@ZhipuAI", "slug": "glm-5-3", "tagline": "Leading bilingual English/Chinese foundation model optimized for enterprise ERP/CRM tool execution, multi-table SQL queries, and logic puzzles.", "category": "Frontier Models & Flagships", "subview_id": "frontier", "architecture": "Autoregressive MoE with Dual-Attention Heads", "parameter_count": "340B total (48B active)", "context_window": "256k tokens", "license": "Commercial API & Open Research Weights", "timeframe": "monthly", "launched_at": "2026-08-28T10:00:00Z", "release_date": "Aug 28, 2026", "formatted_launched_ago": "Aug 28, 2026 (Updated 4d ago)", "launch_score": 95.8, "freshness_decay": 0.94, "integrity_multiplier": 1.08, "composite_velocity": 95.4, "cohort": "latest", "metrics": { "github_stars": 34000, "huggingface_downloads": 880000, "benchmark_mats": "GSM8K 94.2% | BIRD SQL 76.5% | AlignBench 88.9%", "throughput_tokens_sec": 76, "aa_intelligence_index": 51.2 }, "actions": { "try_url": "https://open.bigmodel.cn", "github_url": "https://github.com/THUDM/GLM-4", "paper_or_announcement_url": "https://zhipuai.cn/en" } }, { "id": "rel-f-11", "rank": 11, "name": "DeepSeek-V4.1 Flash High-Speed MoE", "provider": "DeepSeek AI", "maker_handle": "@deepseek_ai", "slug": "deepseek-v4-1-flash", "tagline": "Ultra-low latency open-weights MoE model delivering frontier coding and agentic step execution at 240 tokens/second on 8xH100.", "category": "Frontier Models & Flagships", "subview_id": "frontier", "architecture": "Multi-Head Latent Attention (MLA) Sparse MoE", "parameter_count": "280B (24B active)", "context_window": "128k tokens", "license": "MIT License (Fully Open)", "timeframe": "monthly", "launched_at": "2026-08-22T12:00:00Z", "release_date": "Aug 22, 2026", "formatted_launched_ago": "Aug 22, 2026 (Updated 5d ago)", "launch_score": 95.5, "freshness_decay": 0.93, "integrity_multiplier": 1.12, "composite_velocity": 95.2, "cohort": "latest", "metrics": { "github_stars": 82000, "huggingface_downloads": 2100000, "benchmark_mats": "HumanEval 91.4% | MATH 86.8% | TTFT 28ms", "throughput_tokens_sec": 240, "aa_intelligence_index": 50.8 }, "actions": { "try_url": "https://chat.deepseek.com", "github_url": "https://github.com/deepseek-ai", "huggingface_url": "https://huggingface.co/deepseek-ai", "paper_or_announcement_url": "https://deepseek.com" } }, { "id": "rel-f-12", "rank": 12, "name": "Mistral Large 3 Frontier Multilingual", "provider": "Mistral AI", "maker_handle": "@MistralAI", "slug": "mistral-large-3", "tagline": "European sovereign frontier model with 128k context, native tool use, and parity with proprietary US flagships across French, German, and Spanish.", "category": "Frontier Models & Flagships", "subview_id": "frontier", "architecture": "Dense Decoder-Only Transformer (GQA)", "parameter_count": "160B Dense", "context_window": "128k tokens", "license": "Commercial API & Mistral Research License", "timeframe": "weekly", "launched_at": "2026-09-15T09:00:00Z", "release_date": "Sep 15, 2026", "formatted_launched_ago": "Sep 15, 2026 (Updated 3d ago)", "launch_score": 95.1, "freshness_decay": 0.97, "integrity_multiplier": 1.08, "composite_velocity": 94.9, "cohort": "latest", "metrics": { "github_stars": 29000, "huggingface_downloads": 780000, "benchmark_mats": "MMLU 88.8% | Multi-Language EuroBench 93.4%", "throughput_tokens_sec": 64, "aa_intelligence_index": 51.6 }, "actions": { "try_url": "https://chat.mistral.ai", "github_url": "https://github.com/mistralai", "paper_or_announcement_url": "https://mistral.ai/news" } }, { "id": "rel-f-13", "rank": 13, "name": "Qwen-2.5-Max Flagship MoE", "provider": "Alibaba Cloud", "maker_handle": "@Alibaba_Qwen", "slug": "qwen-2-5-max", "tagline": "Alibaba flagship MoE matching top frontier US models on LMSYS Chatbot Arena with high-precision tool calling and math reasoning.", "category": "Frontier Models & Flagships", "subview_id": "frontier", "architecture": "Massive MoE Transformer with GQA & RoPE", "parameter_count": "Undisclosed Massive MoE", "context_window": "128k tokens", "license": "Commercial API", "timeframe": "weekly", "launched_at": "2026-09-19T08:00:00Z", "release_date": "Sep 19, 2026", "formatted_launched_ago": "Sep 19, 2026 (Updated 2d ago)", "launch_score": 94.8, "freshness_decay": 0.97, "integrity_multiplier": 1.08, "composite_velocity": 94.6, "cohort": "latest", "metrics": { "github_stars": 41000, "huggingface_downloads": 980000, "benchmark_mats": "Arena Elo 1342 | MATH-500 89.4% | GSM8K 95.1%", "throughput_tokens_sec": 88, "aa_intelligence_index": 52.4 }, "actions": { "try_url": "https://chat.qwen.ai", "paper_or_announcement_url": "https://qwenlm.github.io/blog" } }, { "id": "rel-f-14", "rank": 14, "name": "Command R+ v2 Enterprise RAG", "provider": "Cohere", "maker_handle": "@cohere", "slug": "cohere-command-r-plus-v2", "tagline": "Enterprise retrieval-augmented generation engine with native citation groundedness, multi-document cross-referencing, and tabular auditability.", "category": "Frontier Models & Flagships", "subview_id": "frontier", "architecture": "Autoregressive Transformer with Citation Heads", "parameter_count": "110B", "context_window": "128k tokens", "license": "Commercial API & Cohere Research Weights", "timeframe": "weekly", "launched_at": "2026-09-08T14:00:00Z", "release_date": "Sep 8, 2026", "formatted_launched_ago": "Sep 8, 2026 (Updated 4d ago)", "launch_score": 94.2, "freshness_decay": 0.96, "integrity_multiplier": 1.06, "composite_velocity": 93.9, "cohort": "latest", "metrics": { "github_stars": 14500, "huggingface_downloads": 480000, "benchmark_mats": "RAG Groundedness 97.4% | Hallucination Rejection 95.8%", "throughput_tokens_sec": 92, "aa_intelligence_index": 48.9 }, "actions": { "try_url": "https://coral.cohere.com", "github_url": "https://github.com/cohere-ai", "paper_or_announcement_url": "https://cohere.com/blog" } }, { "id": "rel-f-15", "rank": 15, "name": "AI21 Jamba 1.7 Large Hybrid SSM", "provider": "AI21 Labs", "maker_handle": "@AI21Labs", "slug": "ai21-jamba-1-7-large", "tagline": "Hybrid SSM-Transformer architecture delivering 3x higher throughput at 256k context lengths compared to standard attention architectures.", "category": "Frontier Models & Flagships", "subview_id": "frontier", "architecture": "Hybrid Mamba SSM & Transformer Attention MoE", "parameter_count": "388B total (94B active)", "context_window": "256k tokens", "license": "Open Weights & Commercial API", "timeframe": "monthly", "launched_at": "2026-08-19T11:00:00Z", "release_date": "Aug 19, 2026", "formatted_launched_ago": "Aug 19, 2026 (Updated 6d ago)", "launch_score": 93.7, "freshness_decay": 0.92, "integrity_multiplier": 1.08, "composite_velocity": 93.4, "cohort": "latest", "metrics": { "github_stars": 12200, "huggingface_downloads": 390000, "benchmark_mats": "256k Context Needle-In-A-Haystack 99.8% | RULER 88.2%", "throughput_tokens_sec": 145, "aa_intelligence_index": 48.2 }, "actions": { "try_url": "https://studio.ai21.com", "huggingface_url": "https://huggingface.co/ai21labs", "paper_or_announcement_url": "https://ai21.com/blog" } }, { "id": "rel-f-16", "rank": 16, "name": "Baichuan 4 Turbo Domain Reasoning", "provider": "Baichuan AI", "maker_handle": "@BaichuanAI", "slug": "baichuan-4-turbo", "tagline": "Frontier reasoning engine fine-tuned on verified clinical trial corpora and statutory legal documents with strict factual citation verification.", "category": "Frontier Models & Flagships", "subview_id": "frontier", "architecture": "Dense Decoder Transformer with Medical/Legal Priors", "parameter_count": "Undisclosed Dense", "context_window": "128k tokens", "license": "Commercial API", "timeframe": "weekly", "launched_at": "2026-09-10T06:00:00Z", "release_date": "Sep 10, 2026", "formatted_launched_ago": "Sep 10, 2026 (Updated 5d ago)", "launch_score": 93.1, "freshness_decay": 0.96, "integrity_multiplier": 1.05, "composite_velocity": 92.8, "cohort": "latest", "metrics": { "github_stars": 9800, "huggingface_downloads": 240000, "benchmark_mats": "MedQA 89.2% | China Legal QA 91.6%", "throughput_tokens_sec": 84, "aa_intelligence_index": 47.6 }, "actions": { "try_url": "https://baichuan-ai.com", "paper_or_announcement_url": "https://baichuan-ai.com/news" } }, { "id": "rel-f-17", "rank": 17, "name": "MiniMax-abab 7 Conversational MoE", "provider": "MiniMax", "maker_handle": "@MiniMax_AI", "slug": "minimax-abab-7", "tagline": "Next-generation MoE foundation model engineered for emotionally resonant conversational turns, native roleplay, and human prosody mimicry.", "category": "Frontier Models & Flagships", "subview_id": "frontier", "architecture": "Sparse Mixture-of-Experts with Audio Feature Tokens", "parameter_count": "240B (32B active)", "context_window": "200k tokens", "license": "Commercial API", "timeframe": "weekly", "launched_at": "2026-09-05T13:00:00Z", "release_date": "Sep 5, 2026", "formatted_launched_ago": "Sep 5, 2026 (Updated 6d ago)", "launch_score": 92.6, "freshness_decay": 0.95, "integrity_multiplier": 1.05, "composite_velocity": 92.2, "cohort": "latest", "metrics": { "github_stars": 8400, "huggingface_downloads": 180000, "benchmark_mats": "Roleplay Alignment 95.4% | Empathetic Response Index 92.8%", "throughput_tokens_sec": 98, "aa_intelligence_index": 46.8 }, "actions": { "try_url": "https://api.minimax.chat", "paper_or_announcement_url": "https://minimax.chat" } }, { "id": "rel-f-18", "rank": 18, "name": "Snowflake Arctic 2 Enterprise Intelligence", "provider": "Snowflake", "maker_handle": "@SnowflakeDB", "slug": "snowflake-arctic-2", "tagline": "Enterprise open-weights MoE fine-tuned for high-concurrency SQL generation, enterprise data governance, and data catalog schema translation.", "category": "Frontier Models & Flagships", "subview_id": "frontier", "architecture": "Dense-MoE Hybrid with Enterprise SQL Grammar", "parameter_count": "180B total (28B active)", "context_window": "128k tokens", "license": "Apache 2.0", "timeframe": "monthly", "launched_at": "2026-08-16T15:00:00Z", "release_date": "Aug 16, 2026", "formatted_launched_ago": "Aug 16, 2026 (Updated 7d ago)", "launch_score": 92.1, "freshness_decay": 0.91, "integrity_multiplier": 1.06, "composite_velocity": 91.8, "cohort": "latest", "metrics": { "github_stars": 11500, "huggingface_downloads": 410000, "benchmark_mats": "Spider SQL 84.8% | Enterprise Data Governance 96.2%", "throughput_tokens_sec": 115, "aa_intelligence_index": 47.1 }, "actions": { "try_url": "https://snowflake.com/arctic", "github_url": "https://github.com/snowflakedb/snowflake-arctic", "huggingface_url": "https://huggingface.co/Snowflake/snowflake-arctic-instruct", "paper_or_announcement_url": "https://snowflake.com/blog" } }, { "id": "rel-a-19", "rank": 19, "name": "Meta Muse Cross-App Autonomous Agent", "provider": "Meta", "maker_handle": "@Meta", "slug": "meta-muse", "tagline": "Personal cross-app agent operating email, calendar, shopping, and web navigation in secure cloud VM; features live video chat, Mac desktop client, and dedicated personal email address.", "category": "Consumer Agents & Platforms", "subview_id": "agents", "architecture": "Multi-Modal Agent Controller with Sandboxed VM Drivers", "parameter_count": "Autonomous Agent Platform", "context_window": "Continuous Session Memory", "license": "Consumer Service & Developer Connectors", "timeframe": "weekly", "launched_at": "2026-09-08T16:00:00Z", "release_date": "Sep 8, 2026", "formatted_launched_ago": "Sep 8, 2026 (Updated 1d ago)", "launch_score": 99.4, "freshness_decay": 0.97, "integrity_multiplier": 1.15, "composite_velocity": 99.2, "cohort": "latest", "metrics": { "github_stars": 19500, "huggingface_downloads": 520000, "benchmark_mats": "App Action Completion 93.6% | Zero Sandboxed VM Leaks", "throughput_tokens_sec": 65 }, "actions": { "try_url": "https://muse.meta.com", "paper_or_announcement_url": "https://about.fb.com/news" } }, { "id": "rel-a-20", "rank": 20, "name": "Grok Bot Persistent Cloud-Computer Agents", "provider": "xAI", "maker_handle": "@xAI", "slug": "grok-bot", "tagline": "Always-on persistent cloud-computer agent capable of signing into authorized SaaS accounts, running long-running scrapers, and reporting findings to X DMs.", "category": "Consumer Agents & Platforms", "subview_id": "agents", "architecture": "Persistent Cloud Container Agent with Tool Handshakes", "parameter_count": "Autonomous Cloud Agent", "context_window": "Persistent Long-Term Memory", "license": "Commercial xAI Subscription", "timeframe": "monthly", "launched_at": "2026-08-25T18:00:00Z", "release_date": "Aug 25, 2026", "formatted_launched_ago": "Aug 25, 2026 (Updated 3d ago)", "launch_score": 98.2, "freshness_decay": 0.94, "integrity_multiplier": 1.12, "composite_velocity": 97.9, "cohort": "latest", "metrics": { "github_stars": 14200, "huggingface_downloads": 310000, "benchmark_mats": "Autonomous Long-Horizon Uptime 99.4% | Task Success 88.2%", "throughput_tokens_sec": 80 }, "actions": { "try_url": "https://x.ai/grok", "paper_or_announcement_url": "https://x.ai" } }, { "id": "rel-a-21", "rank": 21, "name": "OpenAI Dots Persistent Background Agents", "provider": "OpenAI", "maker_handle": "@OpenAI", "slug": "openai-dots", "tagline": "Autonomous background agent architecture that maintains long-term entity memory across weeks of user workflows, proactively completing recurring chores.", "category": "Consumer Agents & Platforms", "subview_id": "agents", "architecture": "Entity-Centric Semantic State Machine & Scheduler", "parameter_count": "Agent Infrastructure", "context_window": "Cross-Session State Graph", "license": "Proprietary ChatGPT Pro Feature", "timeframe": "weekly", "launched_at": "2026-09-18T17:00:00Z", "release_date": "Sep 18, 2026", "formatted_launched_ago": "Sep 18, 2026 (Updated 2d ago)", "launch_score": 98, "freshness_decay": 0.98, "integrity_multiplier": 1.12, "composite_velocity": 97.8, "cohort": "latest", "metrics": { "github_stars": 28000, "huggingface_downloads": 640000, "benchmark_mats": "Semantic Memory Retention 98.1% | False Wakeup Rate <0.4%", "throughput_tokens_sec": 90 }, "actions": { "try_url": "https://chatgpt.com", "paper_or_announcement_url": "https://openai.com/index" } }, { "id": "rel-a-22", "rank": 22, "name": "Microsoft Autopilot Enterprise Task Layer", "provider": "Microsoft", "maker_handle": "@Microsoft", "slug": "microsoft-autopilot", "tagline": "Enterprise autonomous task orchestration engine executing cross-tenant workflows across Excel, Teams, Outlook, and Windows 11 desktop surfaces.", "category": "Consumer Agents & Platforms", "subview_id": "agents", "architecture": "OS-Integrated Win32/M365 Action Synthesizer", "parameter_count": "Enterprise Agent System", "context_window": "M365 Graph Context", "license": "Commercial Enterprise M365 Addon", "timeframe": "weekly", "launched_at": "2026-09-16T15:00:00Z", "release_date": "Sep 16, 2026", "formatted_launched_ago": "Sep 16, 2026 (Updated 3d ago)", "launch_score": 97.2, "freshness_decay": 0.97, "integrity_multiplier": 1.1, "composite_velocity": 97, "cohort": "latest", "metrics": { "github_stars": 12000, "huggingface_downloads": 410000, "benchmark_mats": "Enterprise Workflow Pass Rate 91.2% | SOC2 Type II Certified", "throughput_tokens_sec": 75 }, "actions": { "try_url": "https://copilot.microsoft.com", "paper_or_announcement_url": "https://blogs.microsoft.com" } }, { "id": "rel-a-23", "rank": 23, "name": "AgentDesk Local Computer-Use Container", "provider": "AgentDesk Open Source", "maker_handle": "@AgentDeskAI", "slug": "agentdesk", "tagline": "Desktop-native sandboxed container allowing local models (Ollama, MiMo, Qwen) to execute full computer-use and browser automations safely.", "category": "Consumer Agents & Platforms", "subview_id": "agents", "architecture": "Docker/WASM GUI Isolation Sandbox with Local VLM Driver", "parameter_count": "Desktop Application", "context_window": "Local Buffer", "license": "MIT License (Open Source)", "timeframe": "monthly", "launched_at": "2026-08-30T14:00:00Z", "release_date": "Aug 30, 2026", "formatted_launched_ago": "Aug 30, 2026 (Updated 5d ago)", "launch_score": 96.4, "freshness_decay": 0.94, "integrity_multiplier": 1.1, "composite_velocity": 96.1, "cohort": "latest", "metrics": { "github_stars": 26500, "huggingface_downloads": 140000, "benchmark_mats": "Zero Host Escape Vulnerabilities | OSWorld Local 64.2%", "throughput_tokens_sec": 50 }, "actions": { "try_url": "https://github.com/agentdesk-ai/agentdesk", "github_url": "https://github.com/agentdesk-ai/agentdesk", "paper_or_announcement_url": "https://github.com/agentdesk-ai/agentdesk/releases" } }, { "id": "rel-a-24", "rank": 24, "name": "CrewAI v1.2 Autonomous Team Orchestration", "provider": "CrewAI Inc", "maker_handle": "@CrewAIInc", "slug": "crewai-1-2", "tagline": "Production multi-agent framework enabling autonomous teams where specialized roles hand off artifacts, execute tool loops, and evaluate outputs.", "category": "Consumer Agents & Platforms", "subview_id": "agents", "architecture": "Role-Based Hierarchical Multi-Agent Graph", "parameter_count": "Framework Library", "context_window": "Dynamic Context Pruning", "license": "MIT License (Open Source)", "timeframe": "weekly", "launched_at": "2026-09-24T18:00:00Z", "release_date": "Sep 24, 2026", "formatted_launched_ago": "Sep 24, 2026 (Updated 2d ago)", "launch_score": 96, "freshness_decay": 0.98, "integrity_multiplier": 1.1, "composite_velocity": 95.8, "cohort": "latest", "metrics": { "github_stars": 38400, "huggingface_downloads": 480000, "benchmark_mats": "Delegation Loop Efficiency 94.6% | Python/TS Native SDKs", "throughput_tokens_sec": 120 }, "actions": { "try_url": "https://crewai.com", "github_url": "https://github.com/crewAIInc/crewAI", "paper_or_announcement_url": "https://docs.crewai.com" } }, { "id": "rel-a-25", "rank": 25, "name": "Hermes Agent OS Decentralized Runtime", "provider": "Nous Research", "maker_handle": "@NousResearch", "slug": "hermes-agent-os", "tagline": "Decentralized open-weights agent runtime with local tool-calling loops, cryptographic execution proofs, and peer-to-peer state synchronization.", "category": "Consumer Agents & Platforms", "subview_id": "agents", "architecture": "Decentralized Micro-Kernel with Function Calling Priors", "parameter_count": "Runtime Framework", "context_window": "Distributed Merkle State", "license": "Apache 2.0", "timeframe": "weekly", "launched_at": "2026-09-12T19:00:00Z", "release_date": "Sep 12, 2026", "formatted_launched_ago": "Sep 12, 2026 (Updated 4d ago)", "launch_score": 95.5, "freshness_decay": 0.96, "integrity_multiplier": 1.1, "composite_velocity": 95.3, "cohort": "latest", "metrics": { "github_stars": 18200, "huggingface_downloads": 290000, "benchmark_mats": "BFCL Tool Calling 92.4% | Merkle Proof Overhead <1ms", "throughput_tokens_sec": 95 }, "actions": { "try_url": "https://nousresearch.com", "github_url": "https://github.com/NousResearch/Hermes-Function-Calling", "paper_or_announcement_url": "https://nousresearch.com/blog" } }, { "id": "rel-a-26", "rank": 26, "name": "Apex.host Serverless Agent Cloud", "provider": "Apex Host", "maker_handle": "@ApexHostAI", "slug": "apex-host-agents", "tagline": "Zero-config serverless cloud platform for deploying 24/7 background AI agents with automatic scaling, persistent browser sessions, and secrets management.", "category": "Consumer Agents & Platforms", "subview_id": "agents", "architecture": "MicroVM Serverless Agent Orchestration Layer", "parameter_count": "Cloud Platform", "context_window": "Continuous Execution Buffer", "license": "Commercial Cloud SaaS", "timeframe": "weekly", "launched_at": "2026-09-20T12:00:00Z", "release_date": "Sep 20, 2026", "formatted_launched_ago": "Sep 20, 2026 (Updated 3d ago)", "launch_score": 95, "freshness_decay": 0.97, "integrity_multiplier": 1.08, "composite_velocity": 94.8, "cohort": "latest", "metrics": { "github_stars": 11200, "huggingface_downloads": 95000, "benchmark_mats": "Cold Start <250ms | Browser Session Persistence 99.8%", "throughput_tokens_sec": 140 }, "actions": { "try_url": "https://apex.host", "paper_or_announcement_url": "https://apex.host/blog" } }, { "id": "rel-a-27", "rank": 27, "name": "Stagehand AI v1.4 Browser Automation SDK", "provider": "Browserbase", "maker_handle": "@browserbase", "slug": "stagehand-v1-4", "tagline": "Self-healing Playwright framework for autonomous web agents with declarative act(), observe(), and extract() primitives resisting DOM layout shifts.", "category": "Consumer Agents & Platforms", "subview_id": "agents", "architecture": "Autonomous DOM Healing Agent Layer", "parameter_count": "TypeScript SDK", "context_window": "N/A", "license": "MIT License", "timeframe": "weekly", "launched_at": "2026-09-21T14:00:00Z", "release_date": "Sep 21, 2026", "formatted_launched_ago": "Sep 21, 2026 (Updated 3d ago)", "launch_score": 94.6, "freshness_decay": 0.98, "integrity_multiplier": 1.08, "composite_velocity": 94.5, "cohort": "latest", "metrics": { "github_stars": 17400, "huggingface_downloads": 120000, "benchmark_mats": "DOM Action Resilience 97.4% | Zero XPath Breakage", "throughput_tokens_sec": 60 }, "actions": { "try_url": "https://stagehand.dev", "github_url": "https://github.com/browserbase/stagehand", "paper_or_announcement_url": "https://stagehand.dev/docs" } }, { "id": "rel-a-28", "rank": 28, "name": "Browser-Use v1.2 Web Agent Controller", "provider": "Browser-Use Open Source", "maker_handle": "@browser_use", "slug": "browser-use-v1-2", "tagline": "Open-source web automation library connecting multi-modal LLMs to Playwright browser automation for end-to-end task execution and parallel scraping.", "category": "Consumer Agents & Platforms", "subview_id": "agents", "architecture": "Vision-DOM Grounding Controller & Playwright Driver", "parameter_count": "Framework Library", "context_window": "N/A", "license": "MIT License", "timeframe": "weekly", "launched_at": "2026-09-26T15:00:00Z", "release_date": "Sep 26, 2026", "formatted_launched_ago": "Sep 26, 2026 (Updated 1d ago)", "launch_score": 94.2, "freshness_decay": 0.99, "integrity_multiplier": 1.08, "composite_velocity": 94.2, "cohort": "latest", "metrics": { "github_stars": 39500, "huggingface_downloads": 240000, "benchmark_mats": "WebVoyager 86.8% | Parallel Multi-Tab Task Completion 92.4%", "throughput_tokens_sec": 55 }, "actions": { "try_url": "https://github.com/browser-use/browser-use", "github_url": "https://github.com/browser-use/browser-use", "paper_or_announcement_url": "https://github.com/browser-use/browser-use/releases" } }, { "id": "rel-a-29", "rank": 29, "name": "Devin Enterprise Autonomous Engineer", "provider": "Cognition", "maker_handle": "@cognition_labs", "slug": "devin-enterprise", "tagline": "Autonomous software engineer with full enterprise monorepo comprehension, automated test suite generation, and secure SOC2 Type II sandboxes.", "category": "Consumer Agents & Platforms", "subview_id": "agents", "architecture": "Autonomous Software Engineering Agent Cluster", "parameter_count": "Multi-Agent Suite", "context_window": "Full Monorepo Context Index", "license": "Enterprise SaaS", "timeframe": "weekly", "launched_at": "2026-09-11T16:00:00Z", "release_date": "Sep 11, 2026", "formatted_launched_ago": "Sep 11, 2026 (Updated 4d ago)", "launch_score": 93.8, "freshness_decay": 0.96, "integrity_multiplier": 1.08, "composite_velocity": 93.5, "cohort": "latest", "metrics": { "github_stars": 22000, "huggingface_downloads": 180000, "benchmark_mats": "SWE-bench Enterprise 48.6% | Zero Out-Of-Bounds PR Commits", "throughput_tokens_sec": 70 }, "actions": { "try_url": "https://devin.ai", "paper_or_announcement_url": "https://cognition.ai/blog" } }, { "id": "rel-a-30", "rank": 30, "name": "Adept ACT-2 Enterprise Action Transformer", "provider": "Adept / Amazon", "maker_handle": "@AdeptAILabs", "slug": "adept-act-2", "tagline": "Action transformer model trained to execute multi-step business software workflows directly through GUI clicks, form fills, and screen reading.", "category": "Consumer Agents & Platforms", "subview_id": "agents", "architecture": "Visual Action Transformer (ACT)", "parameter_count": "Undisclosed Action Model", "context_window": "Visual Sequence Window", "license": "Proprietary Commercial API", "timeframe": "monthly", "launched_at": "2026-08-14T17:00:00Z", "release_date": "Aug 14, 2026", "formatted_launched_ago": "Aug 14, 2026 (Updated 7d ago)", "launch_score": 93.2, "freshness_decay": 0.91, "integrity_multiplier": 1.08, "composite_velocity": 92.8, "cohort": "latest", "metrics": { "github_stars": 15400, "huggingface_downloads": 92000, "benchmark_mats": "Desktop Action Success 89.4% | Salesforce/SAP Navigation 94.2%", "throughput_tokens_sec": 45 }, "actions": { "try_url": "https://adept.ai", "paper_or_announcement_url": "https://adept.ai/blog" } }, { "id": "rel-a-31", "rank": 31, "name": "AutoGPT Platform 2.0 Visual Workflow Builder", "provider": "Significant Gravitas", "maker_handle": "@Auto_GPT", "slug": "autogpt-2-0", "tagline": "Visual block-based builder for continuous multi-step agent workflows with integrated browser actions, vector memory, and webhook triggers.", "category": "Consumer Agents & Platforms", "subview_id": "agents", "architecture": "Visual Graph Workflow Engine & Block Runner", "parameter_count": "Framework & Studio", "context_window": "Modular Block Memory", "license": "MIT License (Open Source)", "timeframe": "weekly", "launched_at": "2026-09-07T14:00:00Z", "release_date": "Sep 7, 2026", "formatted_launched_ago": "Sep 7, 2026 (Updated 5d ago)", "launch_score": 92.8, "freshness_decay": 0.95, "integrity_multiplier": 1.06, "composite_velocity": 92.4, "cohort": "latest", "metrics": { "github_stars": 168000, "huggingface_downloads": 420000, "benchmark_mats": "Graph Loop Reliability 96.1% | 200+ Integrations", "throughput_tokens_sec": 85 }, "actions": { "try_url": "https://agpt.co", "github_url": "https://github.com/Significant-Gravitas/AutoGPT", "paper_or_announcement_url": "https://news.agpt.co" } }, { "id": "rel-a-32", "rank": 32, "name": "MultiOn Agent Engine 3.0 Web Consumer Agent", "provider": "MultiOn", "maker_handle": "@MultiOn_AI", "slug": "multion-3-0", "tagline": "Consumer web agent that navigates flight booking, grocery purchasing, calendar synchronization, and complex checkouts across 500+ consumer websites.", "category": "Consumer Agents & Platforms", "subview_id": "agents", "architecture": "Autonomous Browser Agent with CAPTCHA Solving Layer", "parameter_count": "Agent Cloud API", "context_window": "Session Window", "license": "Commercial API & Chrome Extension", "timeframe": "monthly", "launched_at": "2026-08-26T18:00:00Z", "release_date": "Aug 26, 2026", "formatted_launched_ago": "Aug 26, 2026 (Updated 6d ago)", "launch_score": 92.3, "freshness_decay": 0.93, "integrity_multiplier": 1.06, "composite_velocity": 91.9, "cohort": "latest", "metrics": { "github_stars": 11000, "huggingface_downloads": 85000, "benchmark_mats": "Consumer Checkout Success 92.4% | Booking Speed <45s", "throughput_tokens_sec": 50 }, "actions": { "try_url": "https://multion.ai", "paper_or_announcement_url": "https://multion.ai/blog" } }, { "id": "rel-a-33", "rank": 33, "name": "E2B Sandboxes v2.0 MicroVM Agent Execution", "provider": "E2B", "maker_handle": "@e2b_dev", "slug": "e2b-sandboxes-v2", "tagline": "Secure microVM infrastructure specifically built for AI agents to run untrusted Python, bash commands, and browser sessions with sub-100ms cold starts.", "category": "Consumer Agents & Platforms", "subview_id": "agents", "architecture": "Custom Firecracker MicroVM Execution Sandboxes", "parameter_count": "Infrastructure SDK", "context_window": "Per-Sandbox Ephemeral FS", "license": "Apache 2.0 & Cloud Service", "timeframe": "weekly", "launched_at": "2026-09-14T11:00:00Z", "release_date": "Sep 14, 2026", "formatted_launched_ago": "Sep 14, 2026 (Updated 4d ago)", "launch_score": 91.9, "freshness_decay": 0.97, "integrity_multiplier": 1.08, "composite_velocity": 91.7, "cohort": "latest", "metrics": { "github_stars": 8900, "huggingface_downloads": 140000, "benchmark_mats": "MicroVM Spinup Time 85ms | 100k Concurrent Sandboxes", "throughput_tokens_sec": 250 }, "actions": { "try_url": "https://e2b.dev", "github_url": "https://github.com/e2b-dev/E2B", "paper_or_announcement_url": "https://e2b.dev/docs" } }, { "id": "rel-a-34", "rank": 34, "name": "Open-Interpreter 0.4 Local OS Controller", "provider": "Open Interpreter", "maker_handle": "@OpenInterpreter", "slug": "open-interpreter-0-4", "tagline": "Open-source local terminal interpreter enabling language models to run Python, control GUI mouse/keyboard, and automate local desktop files.", "category": "Consumer Agents & Platforms", "subview_id": "agents", "architecture": "Local Code Execution & OS System Call Bridge", "parameter_count": "Open Source CLI", "context_window": "Local Memory", "license": "AGPL-3.0", "timeframe": "monthly", "launched_at": "2026-08-18T16:00:00Z", "release_date": "Aug 18, 2026", "formatted_launched_ago": "Aug 18, 2026 (Updated 7d ago)", "launch_score": 91.4, "freshness_decay": 0.92, "integrity_multiplier": 1.06, "composite_velocity": 91.1, "cohort": "latest", "metrics": { "github_stars": 56000, "huggingface_downloads": 210000, "benchmark_mats": "Local Terminal Execution 98.2% | System Call Isolation", "throughput_tokens_sec": 75 }, "actions": { "try_url": "https://openinterpreter.com", "github_url": "https://github.com/OpenInterpreter/open-interpreter", "paper_or_announcement_url": "https://docs.openinterpreter.com" } }, { "id": "rel-c-35", "rank": 35, "name": "Claude Code Terminal-Native Agent", "provider": "Anthropic", "maker_handle": "@AnthropicAI", "slug": "claude-code", "tagline": "Terminal-native repo navigation, editing, multi-file refactoring, git commit synthesis, and test runner executing directly inside your command line.", "category": "Coding Agents & Dev Tools", "subview_id": "coding", "architecture": "Terminal-Bound Agent Loop with Subprocess Tooling", "parameter_count": "CLI Agent Tool", "context_window": "Repo-Level Semantic Index", "license": "Proprietary Commercial CLI", "timeframe": "monthly", "launched_at": "2026-07-15T16:00:00Z", "release_date": "Jul 15, 2026", "formatted_launched_ago": "Jul 15, 2026 (Updated 2w ago)", "launch_score": 99.1, "freshness_decay": 0.95, "integrity_multiplier": 1.15, "composite_velocity": 98.9, "cohort": "latest", "metrics": { "github_stars": 48000, "huggingface_downloads": 980000, "benchmark_mats": "SWE-bench Verified 49.8% | Terminal Speed <200ms TTFT", "throughput_tokens_sec": 85 }, "actions": { "try_url": "https://claude.ai/code", "paper_or_announcement_url": "https://anthropic.com/news" } }, { "id": "rel-c-36", "rank": 36, "name": "Cursor Agent Mode Multi-File Engine", "provider": "Anysphere", "maker_handle": "@cursor_ai", "slug": "cursor-agent-mode", "tagline": "Agentic IDE mode with multi-file code generation, automatic background debugging, lint error resolution, and integrated terminal shell control.", "category": "Coding Agents & Dev Tools", "subview_id": "coding", "architecture": "IDE-Integrated Multi-File Diff Engine & Shell Supervisor", "parameter_count": "IDE Agent System", "context_window": "Full Workspace Index", "license": "Proprietary Commercial IDE", "timeframe": "weekly", "launched_at": "2026-09-25T14:00:00Z", "release_date": "Sep 25, 2026", "formatted_launched_ago": "Sep 25, 2026 (Updated 1d ago)", "launch_score": 98.8, "freshness_decay": 0.99, "integrity_multiplier": 1.14, "composite_velocity": 98.7, "cohort": "latest", "metrics": { "github_stars": 64000, "huggingface_downloads": 2400000, "benchmark_mats": "Multi-File Edit Accuracy 94.2% | Debug Loop Resolution 91.8%", "throughput_tokens_sec": 110 }, "actions": { "try_url": "https://cursor.com", "paper_or_announcement_url": "https://cursor.com/blog" } }, { "id": "rel-c-37", "rank": 37, "name": "GitHub Copilot HydraFusion Multi-Model Router", "provider": "GitHub / Microsoft", "maker_handle": "@github", "slug": "github-copilot-hydrafusion", "tagline": "Dynamic coding router orchestrating queries across GPT-6 Astra, Gemini 4 Argon, and Claude 5.5 depending on programming language and syntax depth.", "category": "Coding Agents & Dev Tools", "subview_id": "coding", "architecture": "Multi-Model Inference Router & Fallback Pipeline", "parameter_count": "Enterprise Coding Service", "context_window": "Contextual Workspace Window", "license": "Commercial GitHub Subscription", "timeframe": "weekly", "launched_at": "2026-09-17T15:00:00Z", "release_date": "Sep 17, 2026", "formatted_launched_ago": "Sep 17, 2026 (Updated 3d ago)", "launch_score": 97.9, "freshness_decay": 0.97, "integrity_multiplier": 1.12, "composite_velocity": 97.6, "cohort": "latest", "metrics": { "github_stars": 52000, "huggingface_downloads": 3800000, "benchmark_mats": "Code Acceptance Rate 38.6% | Zero Routing Stalls", "throughput_tokens_sec": 130 }, "actions": { "try_url": "https://github.com/features/copilot", "paper_or_announcement_url": "https://github.blog" } }, { "id": "rel-c-38", "rank": 38, "name": "SWE-2 High-Autonomy Full-Stack PR Manager", "provider": "Cognition", "maker_handle": "@cognition_labs", "slug": "swe-2-pr-manager", "tagline": "Autonomous GitHub PR manager resolving multi-file issues, generating comprehensive regression tests, and passing CI/CD pipelines without human supervision.", "category": "Coding Agents & Dev Tools", "subview_id": "coding", "architecture": "High-Autonomy Software Agent Cluster", "parameter_count": "Autonomous Agent Platform", "context_window": "Full Monorepo Context", "license": "Commercial Enterprise SaaS", "timeframe": "weekly", "launched_at": "2026-09-23T18:00:00Z", "release_date": "Sep 23, 2026", "formatted_launched_ago": "Sep 23, 2026 (Updated 2d ago)", "launch_score": 97.4, "freshness_decay": 0.98, "integrity_multiplier": 1.12, "composite_velocity": 97.2, "cohort": "latest", "metrics": { "github_stars": 24000, "huggingface_downloads": 320000, "benchmark_mats": "SWE-bench Verified 52.4% | Green CI/CD First-Pass 84.1%", "throughput_tokens_sec": 75 }, "actions": { "try_url": "https://cognition.ai", "paper_or_announcement_url": "https://cognition.ai/blog" } }, { "id": "rel-c-39", "rank": 39, "name": "Aider v2.0 Multi-Agent Git Programmer", "provider": "Aider Open Source", "maker_handle": "@AiderAI", "slug": "aider-v2", "tagline": "Git-integrated pair programmer with multi-agent editing, automatic unit test looping, and commit message synthesis supporting 20+ LLM backends.", "category": "Coding Agents & Dev Tools", "subview_id": "coding", "architecture": "Git-Aware Multi-Agent Refactoring Harness", "parameter_count": "Open Source CLI Tool", "context_window": "Git Graph Repository Map", "license": "Apache 2.0", "timeframe": "monthly", "launched_at": "2026-08-29T16:00:00Z", "release_date": "Aug 29, 2026", "formatted_launched_ago": "Aug 29, 2026 (Updated 5d ago)", "launch_score": 96.8, "freshness_decay": 0.94, "integrity_multiplier": 1.1, "composite_velocity": 96.5, "cohort": "latest", "metrics": { "github_stars": 31200, "huggingface_downloads": 410000, "benchmark_mats": "Exercism Benchmark 89.2% | Zero Git Conflict Rate", "throughput_tokens_sec": 90 }, "actions": { "try_url": "https://aider.chat", "github_url": "https://github.com/paul-gauthier/aider", "paper_or_announcement_url": "https://aider.chat/blog" } }, { "id": "rel-c-40", "rank": 40, "name": "OpenAI Codex Revived Full-Stack Synthesizer", "provider": "OpenAI", "maker_handle": "@OpenAI", "slug": "openai-codex-revived", "tagline": "Revived and re-architected Codex engine paired with Astra demos for instantaneous full-stack React/Node app generation from plain english design specs.", "category": "Coding Agents & Dev Tools", "subview_id": "coding", "architecture": "Full-Stack Code Generation Transformer", "parameter_count": "Undisclosed Dense", "context_window": "256k tokens", "license": "Proprietary Commercial API", "timeframe": "weekly", "launched_at": "2026-09-03T18:00:00Z", "release_date": "Sep 3, 2026", "formatted_launched_ago": "Sep 3, 2026 (Updated 1w ago)", "launch_score": 96.3, "freshness_decay": 0.97, "integrity_multiplier": 1.1, "composite_velocity": 96.1, "cohort": "latest", "metrics": { "github_stars": 36000, "huggingface_downloads": 820000, "benchmark_mats": "HumanEval 95.8% | Full-Stack App Build Pass Rate 88.6%", "throughput_tokens_sec": 140 }, "actions": { "try_url": "https://platform.openai.com", "paper_or_announcement_url": "https://openai.com/index" } }, { "id": "rel-c-41", "rank": 41, "name": "AnythingLLM Enterprise SOP & Code Assistant", "provider": "Mintplex Labs", "maker_handle": "@MintplexLabs", "slug": "anythingllm-enterprise", "tagline": "Turn company SOPs, repos, PDFs, and manuals into a searchable assistant with full local offline vector database support and multi-user RBAC.", "category": "Coding Agents & Dev Tools", "subview_id": "coding", "architecture": "Full-Stack Private Enterprise RAG Application", "parameter_count": "Desktop & Server Suite", "context_window": "Dynamic Vector RAG", "license": "MIT License (Open Source)", "timeframe": "weekly", "launched_at": "2026-09-18T13:00:00Z", "release_date": "Sep 18, 2026", "formatted_launched_ago": "Sep 18, 2026 (Updated 3d ago)", "launch_score": 95.8, "freshness_decay": 0.98, "integrity_multiplier": 1.08, "composite_velocity": 95.6, "cohort": "latest", "metrics": { "github_stars": 39000, "huggingface_downloads": 650000, "benchmark_mats": "Local Offline RAG Precision 94.2% | Zero Telemetry Leaks", "throughput_tokens_sec": 180 }, "actions": { "try_url": "https://anythingllm.com", "github_url": "https://github.com/Mintplex-Labs/anything-llm", "paper_or_announcement_url": "https://useanything.com" } }, { "id": "rel-c-42", "rank": 42, "name": "vLLM 0.7.2 High-Throughput Serving Engine", "provider": "vLLM Project", "maker_handle": "@vllm_project", "slug": "vllm-0-7-2", "tagline": "Major serving upgrade featuring chunked prefill, FP8 KV cache quantization, and speculative decoding acceleration boosting token throughput by 3.8x.", "category": "Coding Agents & Dev Tools", "subview_id": "coding", "architecture": "PagedAttention GPU Kernel Architecture", "parameter_count": "Inference Serving Library", "context_window": "N/A", "license": "Apache 2.0", "timeframe": "weekly", "launched_at": "2026-09-25T11:00:00Z", "release_date": "Sep 25, 2026", "formatted_launched_ago": "Sep 25, 2026 (Updated 1d ago)", "launch_score": 95.4, "freshness_decay": 0.99, "integrity_multiplier": 1.08, "composite_velocity": 95.3, "cohort": "latest", "metrics": { "github_stars": 42500, "huggingface_downloads": 1450000, "benchmark_mats": "Throughput 3.8x v0.5.0 | TTFT 29ms | FP8 KV Acceleration", "throughput_tokens_sec": 2600 }, "actions": { "try_url": "https://github.com/vllm-project/vllm", "github_url": "https://github.com/vllm-project/vllm", "paper_or_announcement_url": "https://github.com/vllm-project/vllm/releases" } }, { "id": "rel-c-43", "rank": 43, "name": "Ollama 0.6.0 Local Model Runtime", "provider": "Ollama Project", "maker_handle": "@ollama", "slug": "ollama-0-6-0", "tagline": "Local model engine with multi-GPU tensor parallelism, streaming JSON schema validation, vision models, and instant cold-start containerization.", "category": "Coding Agents & Dev Tools", "subview_id": "coding", "architecture": "llama.cpp Core with Custom Go HTTP Server", "parameter_count": "Local Runtime", "context_window": "Up to 256k", "license": "MIT License", "timeframe": "daily", "launched_at": "2026-09-27T16:00:00Z", "release_date": "Sep 27, 2026", "formatted_launched_ago": "Sep 27, 2026 (Updated 14h ago)", "launch_score": 95, "freshness_decay": 0.99, "integrity_multiplier": 1.08, "composite_velocity": 95.1, "cohort": "latest", "metrics": { "github_stars": 122000, "huggingface_downloads": 4800000, "benchmark_mats": "Local Memory Footprint <40MB | Multi-GPU Tensor Parallel", "throughput_tokens_sec": 145 }, "actions": { "try_url": "https://ollama.com", "github_url": "https://github.com/ollama/ollama", "paper_or_announcement_url": "https://ollama.com/blog" } }, { "id": "rel-c-44", "rank": 44, "name": "Qwen 2.5 Coder 32B Instruct", "provider": "Alibaba Qwen", "maker_handle": "@Alibaba_Qwen", "slug": "qwen-2-5-coder-32b", "tagline": "Leading open-weights coding model matching proprietary flagships on HumanEval and MultiPL-E with native 128k context and function calling.", "category": "Coding Agents & Dev Tools", "subview_id": "coding", "architecture": "Dense Decoder-Only Transformer (GQA, RoPE)", "parameter_count": "32.5B", "context_window": "128k tokens", "license": "Apache 2.0", "timeframe": "weekly", "launched_at": "2026-09-20T10:00:00Z", "release_date": "Sep 20, 2026", "formatted_launched_ago": "Sep 20, 2026 (Updated 3d ago)", "launch_score": 94.7, "freshness_decay": 0.98, "integrity_multiplier": 1.08, "composite_velocity": 94.6, "cohort": "latest", "metrics": { "github_stars": 34500, "huggingface_downloads": 1650000, "benchmark_mats": "HumanEval 92.7% | MultiPL-E 83.1% | LiveCodeBench 51.6%", "throughput_tokens_sec": 84 }, "actions": { "try_url": "https://huggingface.co/Qwen/Qwen2.5-Coder-32B-Instruct", "github_url": "https://github.com/QwenLM/Qwen2.5-Coder", "huggingface_url": "https://huggingface.co/Qwen/Qwen2.5-Coder-32B-Instruct", "paper_or_announcement_url": "https://qwenlm.github.io/blog/qwen2.5-coder-family" } }, { "id": "rel-c-45", "rank": 45, "name": "DeepSeek Coder V3 Multi-Token Synthesis", "provider": "DeepSeek AI", "maker_handle": "@deepseek_ai", "slug": "deepseek-coder-v3", "tagline": "Code synthesis MoE with repo-level context compression, AST verification, and multi-token prediction heads for accelerated autocompletion.", "category": "Coding Agents & Dev Tools", "subview_id": "coding", "architecture": "Multi-Token Prediction Sparse MoE with MLA", "parameter_count": "236B total (21B active)", "context_window": "128k tokens", "license": "MIT License (Open Source)", "timeframe": "weekly", "launched_at": "2026-09-12T14:00:00Z", "release_date": "Sep 12, 2026", "formatted_launched_ago": "Sep 12, 2026 (Updated 4d ago)", "launch_score": 94.3, "freshness_decay": 0.96, "integrity_multiplier": 1.08, "composite_velocity": 94.1, "cohort": "latest", "metrics": { "github_stars": 49000, "huggingface_downloads": 1850000, "benchmark_mats": "LiveCodeBench 53.4% | RepoBench 88.6% | AST Parity 98.4%", "throughput_tokens_sec": 195 }, "actions": { "try_url": "https://chat.deepseek.com", "github_url": "https://github.com/deepseek-ai", "huggingface_url": "https://huggingface.co/deepseek-ai", "paper_or_announcement_url": "https://deepseek.com" } }, { "id": "rel-c-46", "rank": 46, "name": "Codeium Windsurf 2.0 Cascade Agentic IDE", "provider": "Codeium", "maker_handle": "@codeiumdev", "slug": "windsurf-2-0", "tagline": "Cascade agentic IDE flows combining AI terminal actions and multi-file code diffs with real-time awareness of developer edit intent.", "category": "Coding Agents & Dev Tools", "subview_id": "coding", "architecture": "Cascade Multi-Modal Developer Agent Environment", "parameter_count": "Desktop Application", "context_window": "Full Workspace Context", "license": "Commercial Subscription & Free Tier", "timeframe": "weekly", "launched_at": "2026-09-22T15:00:00Z", "release_date": "Sep 22, 2026", "formatted_launched_ago": "Sep 22, 2026 (Updated 2d ago)", "launch_score": 93.9, "freshness_decay": 0.98, "integrity_multiplier": 1.06, "composite_velocity": 93.7, "cohort": "latest", "metrics": { "github_stars": 28000, "huggingface_downloads": 890000, "benchmark_mats": "Cascade Completion Acceptance 42.1% | Terminal Loop Zero Stalls", "throughput_tokens_sec": 105 }, "actions": { "try_url": "https://codeium.com/windsurf", "paper_or_announcement_url": "https://codeium.com/blog" } }, { "id": "rel-c-47", "rank": 47, "name": "Continue.dev 1.1 Open Source AI Assistant", "provider": "Continue Open Source", "maker_handle": "@continue_dev", "slug": "continue-dev-1-1", "tagline": "Open-source AI code assistant for VS Code and JetBrains with full support for custom MCP servers, local models, and proprietary APIs.", "category": "Coding Agents & Dev Tools", "subview_id": "coding", "architecture": "Modular Extension Harness with MCP Support", "parameter_count": "IDE Plugin", "context_window": "Dynamic Context Providers", "license": "Apache 2.0", "timeframe": "monthly", "launched_at": "2026-08-22T16:00:00Z", "release_date": "Aug 22, 2026", "formatted_launched_ago": "Aug 22, 2026 (Updated 6d ago)", "launch_score": 93.4, "freshness_decay": 0.93, "integrity_multiplier": 1.06, "composite_velocity": 93.1, "cohort": "latest", "metrics": { "github_stars": 24500, "huggingface_downloads": 320000, "benchmark_mats": "MCP Server Latency <12ms | Multi-File Context Sharing 94.6%", "throughput_tokens_sec": 120 }, "actions": { "try_url": "https://continue.dev", "github_url": "https://github.com/continuedev/continue", "paper_or_announcement_url": "https://docs.continue.dev" } }, { "id": "rel-c-48", "rank": 48, "name": "SGLang 0.4 RadixAttention Serving Engine", "provider": "LMSYS Org", "maker_handle": "@lmsysorg", "slug": "sglang-0-4", "tagline": "RadixAttention serving engine designed for complex multi-call LLM workflows, caching common prefix KV states across concurrent agent turns.", "category": "Coding Agents & Dev Tools", "subview_id": "coding", "architecture": "RadixAttention KV Reuse Engine & Compiler", "parameter_count": "Serving Framework", "context_window": "N/A", "license": "Apache 2.0", "timeframe": "weekly", "launched_at": "2026-09-16T12:00:00Z", "release_date": "Sep 16, 2026", "formatted_launched_ago": "Sep 16, 2026 (Updated 3d ago)", "launch_score": 93, "freshness_decay": 0.97, "integrity_multiplier": 1.06, "composite_velocity": 92.7, "cohort": "latest", "metrics": { "github_stars": 12400, "huggingface_downloads": 195000, "benchmark_mats": "Prefix KV Cache Hit Rate 88.4% | Serving Speed 5x Native", "throughput_tokens_sec": 2100 }, "actions": { "try_url": "https://github.com/sgl-project/sglang", "github_url": "https://github.com/sgl-project/sglang", "paper_or_announcement_url": "https://sgl-project.github.io" } }, { "id": "rel-c-49", "rank": 49, "name": "Repack AI Zero-Overhead C++ Packager", "provider": "Repack Project", "maker_handle": "@RepackAI", "slug": "repack-ai", "tagline": "Zero-overhead binary packager converting PyTorch models into standalone C++ binaries running without Python or CUDA runtime dependencies.", "category": "Coding Agents & Dev Tools", "subview_id": "coding", "architecture": "Static C++ Model Compiler & Weight Packager", "parameter_count": "Developer Compiler", "context_window": "N/A", "license": "MIT License", "timeframe": "monthly", "launched_at": "2026-08-12T14:00:00Z", "release_date": "Aug 12, 2026", "formatted_launched_ago": "Aug 12, 2026 (Updated 8d ago)", "launch_score": 92.5, "freshness_decay": 0.9, "integrity_multiplier": 1.05, "composite_velocity": 92.1, "cohort": "latest", "metrics": { "github_stars": 9400, "huggingface_downloads": 82000, "benchmark_mats": "Binary Footprint Reduction 64% | Zero Python Dependency", "throughput_tokens_sec": 320 }, "actions": { "try_url": "https://github.com/repack-ai/repack", "github_url": "https://github.com/repack-ai/repack", "paper_or_announcement_url": "https://repack.ai" } }, { "id": "rel-c-50", "rank": 50, "name": "TensorRT-LLM 0.14 Blackwell Acceleration", "provider": "NVIDIA", "maker_handle": "@nvidia", "slug": "tensorrt-llm-0-14", "tagline": "Blackwell GPU architecture optimization kernels, native FP4 tensor execution, and speculative decoding yielding 2.4x throughput over Hopper.", "category": "Coding Agents & Dev Tools", "subview_id": "coding", "architecture": "Blackwell Tensor Core Optimized Inference Library", "parameter_count": "Inference Engine", "context_window": "N/A", "license": "NVIDIA Open Source License", "timeframe": "weekly", "launched_at": "2026-09-19T17:00:00Z", "release_date": "Sep 19, 2026", "formatted_launched_ago": "Sep 19, 2026 (Updated 3d ago)", "launch_score": 92, "freshness_decay": 0.97, "integrity_multiplier": 1.06, "composite_velocity": 91.8, "cohort": "latest", "metrics": { "github_stars": 14800, "huggingface_downloads": 620000, "benchmark_mats": "FP4 Throughput 2.4x H100 | Blackwell B200 Optimized", "throughput_tokens_sec": 3800 }, "actions": { "try_url": "https://github.com/NVIDIA/TensorRT-LLM", "github_url": "https://github.com/NVIDIA/TensorRT-LLM", "paper_or_announcement_url": "https://developer.nvidia.com/tensorrt" } }, { "id": "rel-o-51", "rank": 51, "name": "MiMo-V2.6-Pro 1.02T Open-Weights Flagship", "provider": "Xiaomi AI", "maker_handle": "@Xiaomi", "slug": "mimo-v2-6-pro", "tagline": "1.02T total / 42B active parameter MoE; scores 46.32 on Artificial Analysis Intelligence Index v4.3.2, setting the world record for open-source AI.", "category": "Open-Weights & MoE Engines", "subview_id": "open-weights", "architecture": "Sparse Mixture-of-Experts with Multi-Token Heads", "parameter_count": "1.02T (42B active)", "context_window": "1M tokens", "license": "MIT License (Fully Open)", "timeframe": "daily", "launched_at": "2026-09-21T08:00:00Z", "release_date": "Sep 21, 2026", "formatted_launched_ago": "Sep 21, 2026 (Updated 2d ago)", "launch_score": 99.5, "freshness_decay": 0.98, "integrity_multiplier": 1.15, "composite_velocity": 99.4, "cohort": "latest", "metrics": { "github_stars": 38000, "huggingface_downloads": 1450000, "benchmark_mats": "AA Index 46.32 (#1 Open) | AIME 78.4% | MATH 94.6%", "throughput_tokens_sec": 72, "aa_intelligence_index": 46.32 }, "actions": { "try_url": "https://huggingface.co/Xiaomi/MiMo-V2.6-Pro", "github_url": "https://github.com/xiaomi/mimo", "huggingface_url": "https://huggingface.co/Xiaomi/MiMo-V2.6-Pro", "paper_or_announcement_url": "https://ai.xiaomi.com" } }, { "id": "rel-o-52", "rank": 52, "name": "MiMo-V2.6-Flash UltraSpeed MoE", "provider": "Xiaomi AI", "maker_handle": "@Xiaomi", "slug": "mimo-v2-6-flash", "tagline": "309B total / 15B active parameter MoE; features Pro-UltraSpeed inference running 20x faster than Pro with identical high-reasoning quality.", "category": "Open-Weights & MoE Engines", "subview_id": "open-weights", "architecture": "Sparse Mixture-of-Experts (Fast Routing)", "parameter_count": "309B (15B active)", "context_window": "500k tokens", "license": "MIT License (Fully Open)", "timeframe": "daily", "launched_at": "2026-09-22T08:00:00Z", "release_date": "Sep 22, 2026", "formatted_launched_ago": "Sep 22, 2026 (Updated 2d ago)", "launch_score": 98.9, "freshness_decay": 0.98, "integrity_multiplier": 1.14, "composite_velocity": 98.8, "cohort": "latest", "metrics": { "github_stars": 29500, "huggingface_downloads": 980000, "benchmark_mats": "Speedup 20x | TTFT 22ms | HumanEval 89.4%", "throughput_tokens_sec": 260, "aa_intelligence_index": 44.8 }, "actions": { "try_url": "https://huggingface.co/Xiaomi/MiMo-V2.6-Flash", "github_url": "https://github.com/xiaomi/mimo", "huggingface_url": "https://huggingface.co/Xiaomi/MiMo-V2.6-Flash", "paper_or_announcement_url": "https://ai.xiaomi.com" } }, { "id": "rel-o-53", "rank": 53, "name": "Qwen3.8-Omni-Max Multimodal Open MoE", "provider": "Alibaba Qwen", "maker_handle": "@Alibaba_Qwen", "slug": "qwen-3-8-omni-max", "tagline": "1M context multimodal open MoE supporting real-time streaming audio, video comprehension, web browsing, and UI manipulation in weights.", "category": "Open-Weights & MoE Engines", "subview_id": "open-weights", "architecture": "Multimodal Sparse Mixture-of-Experts", "parameter_count": "512B total (64B active)", "context_window": "1M tokens", "license": "Apache 2.0", "timeframe": "daily", "launched_at": "2026-09-27T10:00:00Z", "release_date": "Sep 27, 2026", "formatted_launched_ago": "Sep 27, 2026 (Updated 18h ago)", "launch_score": 98.5, "freshness_decay": 0.99, "integrity_multiplier": 1.14, "composite_velocity": 98.4, "cohort": "latest", "metrics": { "github_stars": 52000, "huggingface_downloads": 1850000, "benchmark_mats": "Video-MME 86.4% | MMMU 74.8% | Native Audio Latency 90ms", "throughput_tokens_sec": 85, "aa_intelligence_index": 45.9 }, "actions": { "try_url": "https://huggingface.co/Qwen/Qwen3.8-Omni-Max", "github_url": "https://github.com/QwenLM/Qwen", "huggingface_url": "https://huggingface.co/Qwen/Qwen3.8-Omni-Max", "paper_or_announcement_url": "https://qwenlm.github.io/blog" } }, { "id": "rel-o-54", "rank": 54, "name": "Qwen3.8-Omni-Flash Edge Multimodal", "provider": "Alibaba Qwen", "maker_handle": "@Alibaba_Qwen", "slug": "qwen-3-8-omni-flash", "tagline": "Compact multimodal audio-visual open weights designed for edge devices, delivering real-time camera feed analysis on laptop GPUs.", "category": "Open-Weights & MoE Engines", "subview_id": "open-weights", "architecture": "Compact Multimodal Transformer (Vision & Audio)", "parameter_count": "28B Dense", "context_window": "256k tokens", "license": "Apache 2.0", "timeframe": "weekly", "launched_at": "2026-09-26T12:00:00Z", "release_date": "Sep 26, 2026", "formatted_launched_ago": "Sep 26, 2026 (Updated 1d ago)", "launch_score": 97.8, "freshness_decay": 0.99, "integrity_multiplier": 1.12, "composite_velocity": 97.6, "cohort": "latest", "metrics": { "github_stars": 36000, "huggingface_downloads": 1100000, "benchmark_mats": "Real-Time Frame Rate 30fps | Edge VRAM 14GB", "throughput_tokens_sec": 140, "aa_intelligence_index": 42.4 }, "actions": { "try_url": "https://huggingface.co/Qwen/Qwen3.8-Omni-Flash", "github_url": "https://github.com/QwenLM/Qwen", "huggingface_url": "https://huggingface.co/Qwen/Qwen3.8-Omni-Flash", "paper_or_announcement_url": "https://qwenlm.github.io/blog" } }, { "id": "rel-o-55", "rank": 55, "name": "Kimi K3 2.8T Native Context MoE", "provider": "Moonshot AI", "maker_handle": "@MoonshotAI", "slug": "kimi-k3-moe", "tagline": "2.8T MoE with 1M native context, visual grounding, and long-horizon tool execution; scored 45.1 on Artificial Analysis Intelligence Index.", "category": "Open-Weights & MoE Engines", "subview_id": "open-weights", "architecture": "Sparse Mixture-of-Experts with Multi-Query Attention", "parameter_count": "2.8T (64B active)", "context_window": "1M tokens", "license": "Moonshot Research & Commercial API", "timeframe": "monthly", "launched_at": "2026-07-28T14:00:00Z", "release_date": "Jul 28, 2026", "formatted_launched_ago": "Jul 28, 2026 (Updated 2w ago)", "launch_score": 97.2, "freshness_decay": 0.92, "integrity_multiplier": 1.12, "composite_velocity": 96.8, "cohort": "latest", "metrics": { "github_stars": 28500, "huggingface_downloads": 820000, "benchmark_mats": "AA Index 45.1 | 1M Context Needle 99.9% | Long CoT Math 88.4%", "throughput_tokens_sec": 64, "aa_intelligence_index": 45.1 }, "actions": { "try_url": "https://kimi.moonshot.cn", "paper_or_announcement_url": "https://moonshot.cn" } }, { "id": "rel-o-56", "rank": 56, "name": "Kimi K2.6 Mathematical Reasoning Engine", "provider": "Moonshot AI", "maker_handle": "@MoonshotAI", "slug": "kimi-k2-6", "tagline": "Reinforcement learning CoT model fine-tuned specifically for formal mathematical deduction, Lean 4 proofs, and competitive programming.", "category": "Open-Weights & MoE Engines", "subview_id": "open-weights", "architecture": "Dense CoT Reasoning Transformer", "parameter_count": "72B Dense", "context_window": "256k tokens", "license": "Open Weights & Research License", "timeframe": "monthly", "launched_at": "2026-08-15T11:00:00Z", "release_date": "Aug 15, 2026", "formatted_launched_ago": "Aug 15, 2026 (Updated 7d ago)", "launch_score": 96.7, "freshness_decay": 0.91, "integrity_multiplier": 1.1, "composite_velocity": 96.3, "cohort": "latest", "metrics": { "github_stars": 18200, "huggingface_downloads": 450000, "benchmark_mats": "AIME 82.4% | Lean 4 Theorem Proving 46.2%", "throughput_tokens_sec": 78, "aa_intelligence_index": 44.2 }, "actions": { "try_url": "https://kimi.moonshot.cn", "huggingface_url": "https://huggingface.co/moonshotai", "paper_or_announcement_url": "https://moonshot.cn" } }, { "id": "rel-o-57", "rank": 57, "name": "Qwen3.6-FP8 Native Quantized Enterprise", "provider": "Alibaba Qwen", "maker_handle": "@Alibaba_Qwen", "slug": "qwen-3-6-fp8", "tagline": "Native FP8 quantized checkpoint designed to fit on a single 8xH100 node without precision loss on MMLU or coding benchmarks.", "category": "Open-Weights & MoE Engines", "subview_id": "open-weights", "architecture": "Native FP8 Quantized Transformer", "parameter_count": "110B FP8", "context_window": "128k tokens", "license": "Apache 2.0", "timeframe": "monthly", "launched_at": "2026-08-31T09:00:00Z", "release_date": "Aug 31, 2026", "formatted_launched_ago": "Aug 31, 2026 (Updated 5d ago)", "launch_score": 96.2, "freshness_decay": 0.94, "integrity_multiplier": 1.1, "composite_velocity": 95.9, "cohort": "latest", "metrics": { "github_stars": 22000, "huggingface_downloads": 720000, "benchmark_mats": "FP8 Perplexity Loss <0.02 | Memory Savings 50%", "throughput_tokens_sec": 185 }, "actions": { "try_url": "https://huggingface.co/Qwen", "github_url": "https://github.com/QwenLM/Qwen", "huggingface_url": "https://huggingface.co/Qwen", "paper_or_announcement_url": "https://qwenlm.github.io/blog" } }, { "id": "rel-o-58", "rank": 58, "name": "Yi-X-Large Math & Logic MoE", "provider": "01.AI", "maker_handle": "@01_AI", "slug": "yi-x-large", "tagline": "Math proofs + multilingual reasoning MoE trained on curated synthetic data pipelines with high bilingual English-Chinese fidelity.", "category": "Open-Weights & MoE Engines", "subview_id": "open-weights", "architecture": "High-Density Mixture-of-Experts", "parameter_count": "240B (32B active)", "context_window": "200k tokens", "license": "Yi Open Source Community License", "timeframe": "monthly", "launched_at": "2026-08-24T15:00:00Z", "release_date": "Aug 24, 2026", "formatted_launched_ago": "Aug 24, 2026 (Updated 6d ago)", "launch_score": 95.8, "freshness_decay": 0.93, "integrity_multiplier": 1.08, "composite_velocity": 95.4, "cohort": "latest", "metrics": { "github_stars": 19500, "huggingface_downloads": 380000, "benchmark_mats": "GSM8K 92.8% | MATH 84.1% | HumanEval 86.4%", "throughput_tokens_sec": 92, "aa_intelligence_index": 43.8 }, "actions": { "try_url": "https://01.ai", "github_url": "https://github.com/01-ai/Yi", "huggingface_url": "https://huggingface.co/01-ai", "paper_or_announcement_url": "https://01.ai/blog" } }, { "id": "rel-o-59", "rank": 59, "name": "Hy-Image-3.5 Ultra-HD Open Diffusion", "provider": "Tencent AI Lab", "maker_handle": "@Tencent", "slug": "hy-image-3-5", "tagline": "Ultra-high resolution open-weights text-to-image foundation model with native bilingual Chinese/English typography and rectified flow architecture.", "category": "Open-Weights & MoE Engines", "subview_id": "open-weights", "architecture": "Rectified Flow Multimodal Diffusion Transformer", "parameter_count": "16B", "context_window": "2048 text tokens", "license": "Tencent Research Open Weights", "timeframe": "weekly", "launched_at": "2026-09-23T11:00:00Z", "release_date": "Sep 23, 2026", "formatted_launched_ago": "Sep 23, 2026 (Updated 2d ago)", "launch_score": 95.3, "freshness_decay": 0.98, "integrity_multiplier": 1.08, "composite_velocity": 95.1, "cohort": "latest", "metrics": { "github_stars": 26000, "huggingface_downloads": 620000, "benchmark_mats": "GenEval 0.88 | Dual Typography Accuracy 96.2%", "throughput_tokens_sec": 38 }, "actions": { "try_url": "https://huggingface.co/Tencent-Hunyuan/HunyuanImage", "github_url": "https://github.com/Tencent/HunyuanImage", "huggingface_url": "https://huggingface.co/Tencent-Hunyuan/HunyuanImage", "paper_or_announcement_url": "https://hunyuan.tencent.com" } }, { "id": "rel-o-60", "rank": 60, "name": "DeepSeek-V4 Pro 1.6T Architecture Preview", "provider": "DeepSeek AI", "maker_handle": "@deepseek_ai", "slug": "deepseek-v4-pro-preview", "tagline": "1.6T parameter MoE architecture with MLA and multi-token prediction; architectural technical report and benchmark checkpoint published.", "category": "Open-Weights & MoE Engines", "subview_id": "open-weights", "architecture": "1.6T Multi-Head Latent Attention Sparse MoE", "parameter_count": "1.6T (64B active)", "context_window": "256k tokens", "license": "MIT License (Open Weights)", "timeframe": "weekly", "launched_at": "2026-09-15T12:00:00Z", "release_date": "Sep 15, 2026", "formatted_launched_ago": "Sep 15, 2026 (Updated 3d ago)", "launch_score": 95, "freshness_decay": 0.97, "integrity_multiplier": 1.1, "composite_velocity": 94.8, "cohort": "latest", "metrics": { "github_stars": 88000, "huggingface_downloads": 2400000, "benchmark_mats": "FrontierMath Tier 3 88.4% | MLA Compression Ratio 6.8x", "throughput_tokens_sec": 68, "aa_intelligence_index": 46.8 }, "actions": { "try_url": "https://chat.deepseek.com", "github_url": "https://github.com/deepseek-ai", "huggingface_url": "https://huggingface.co/deepseek-ai", "paper_or_announcement_url": "https://deepseek.com" } }, { "id": "rel-o-61", "rank": 61, "name": "Llama 3.3 70B Instruct Workhorse", "provider": "Meta AI", "maker_handle": "@MetaAI", "slug": "llama-3-3-70b-instruct", "tagline": "State-of-the-art 70B instruction model matching previous generation 405B capabilities across MMLU and coding benchmarks.", "category": "Open-Weights & MoE Engines", "subview_id": "open-weights", "architecture": "Dense Auto-regressive Transformer (GQA 8-KV heads)", "parameter_count": "70.6B", "context_window": "128k tokens", "license": "Llama 3.3 Community License", "timeframe": "weekly", "launched_at": "2026-09-18T10:00:00Z", "release_date": "Sep 18, 2026", "formatted_launched_ago": "Sep 18, 2026 (Updated 3d ago)", "launch_score": 94.6, "freshness_decay": 0.97, "integrity_multiplier": 1.08, "composite_velocity": 94.4, "cohort": "latest", "metrics": { "github_stars": 79000, "huggingface_downloads": 3200000, "benchmark_mats": "MMLU 88.6% | MATH 73.1% | IFEval 89.4%", "throughput_tokens_sec": 78 }, "actions": { "try_url": "https://llama.meta.com", "github_url": "https://github.com/meta-llama/llama3", "huggingface_url": "https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct", "paper_or_announcement_url": "https://ai.meta.com/blog/llama-3-3-70b" } }, { "id": "rel-o-62", "rank": 62, "name": "Mistral NeMo 2 12B Multilingual", "provider": "Mistral AI", "maker_handle": "@MistralAI", "slug": "mistral-nemo-2-12b", "tagline": "12B multilingual model trained in collaboration with NVIDIA with 128k context and TEKKEN tokenizer for enhanced European token efficiency.", "category": "Open-Weights & MoE Engines", "subview_id": "open-weights", "architecture": "Dense Decoder Transformer with TEKKEN Tokenizer", "parameter_count": "12.2B", "context_window": "128k tokens", "license": "Apache 2.0", "timeframe": "monthly", "launched_at": "2026-08-14T14:00:00Z", "release_date": "Aug 14, 2026", "formatted_launched_ago": "Aug 14, 2026 (Updated 7d ago)", "launch_score": 94.1, "freshness_decay": 0.91, "integrity_multiplier": 1.06, "composite_velocity": 93.8, "cohort": "latest", "metrics": { "github_stars": 18500, "huggingface_downloads": 1250000, "benchmark_mats": "Token Compression 30% | MMLU 71.4%", "throughput_tokens_sec": 160 }, "actions": { "try_url": "https://chat.mistral.ai", "github_url": "https://github.com/mistralai", "huggingface_url": "https://huggingface.co/mistralai/Mistral-Nemo-Instruct-2407", "paper_or_announcement_url": "https://mistral.ai/news" } }, { "id": "rel-o-63", "rank": 63, "name": "SmolLM2 1.7B On-Device Reasoning", "provider": "Hugging Face", "maker_handle": "@huggingface", "slug": "smollm2-1-7b-instruct", "tagline": "Compact on-device reasoning and instruction model trained on 11 trillion tokens, outperforming Llama 3.2 1B and Qwen 2.5 1.5B.", "category": "Open-Weights & MoE Engines", "subview_id": "open-weights", "architecture": "Compact Dense Transformer for Edge Devices", "parameter_count": "1.71B", "context_window": "8k tokens", "license": "Apache 2.0", "timeframe": "weekly", "launched_at": "2026-09-10T12:00:00Z", "release_date": "Sep 10, 2026", "formatted_launched_ago": "Sep 10, 2026 (Updated 5d ago)", "launch_score": 93.7, "freshness_decay": 0.96, "integrity_multiplier": 1.06, "composite_velocity": 93.5, "cohort": "latest", "metrics": { "github_stars": 16400, "huggingface_downloads": 1850000, "benchmark_mats": "MMLU 53.6% | GSM8K 65.2% | Edge RAM <1.2GB", "throughput_tokens_sec": 210 }, "actions": { "try_url": "https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B-Instruct", "github_url": "https://github.com/huggingface/smollm", "huggingface_url": "https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B-Instruct", "paper_or_announcement_url": "https://huggingface.co/blog/smollm2" } }, { "id": "rel-o-64", "rank": 64, "name": "Gemma 3 27B IT Research Open Weights", "provider": "Google DeepMind", "maker_handle": "@GoogleDeepMind", "slug": "gemma-3-27b-it", "tagline": "Open-weights research model distilled from Gemini 3.8 frontier training runs with mathematical reasoning and multi-turn alignment.", "category": "Open-Weights & MoE Engines", "subview_id": "open-weights", "architecture": "Dense Decoder Transformer with Sliding Window Attention", "parameter_count": "27.2B", "context_window": "128k tokens", "license": "Gemma Terms of Use", "timeframe": "weekly", "launched_at": "2026-09-08T11:00:00Z", "release_date": "Sep 8, 2026", "formatted_launched_ago": "Sep 8, 2026 (Updated 5d ago)", "launch_score": 93.3, "freshness_decay": 0.95, "integrity_multiplier": 1.06, "composite_velocity": 93.1, "cohort": "latest", "metrics": { "github_stars": 24000, "huggingface_downloads": 1400000, "benchmark_mats": "MMLU 79.4% | MATH 68.2% | HumanEval 81.6%", "throughput_tokens_sec": 95 }, "actions": { "try_url": "https://ai.google.dev/gemma", "github_url": "https://github.com/google-deepmind/gemma", "huggingface_url": "https://huggingface.co/google/gemma-2-27b-it", "paper_or_announcement_url": "https://deepmind.google/technologies/gemma" } }, { "id": "rel-o-65", "rank": 65, "name": "Gemma 3 9B Flash Edge Engine", "provider": "Google DeepMind", "maker_handle": "@GoogleDeepMind", "slug": "gemma-3-9b-flash", "tagline": "High-throughput open-weights edge model optimized for function calling, structured JSON output, and local desktop copilots.", "category": "Open-Weights & MoE Engines", "subview_id": "open-weights", "architecture": "Compact Dense Decoder with Knowledge Distillation", "parameter_count": "9.2B", "context_window": "128k tokens", "license": "Gemma Terms of Use", "timeframe": "weekly", "launched_at": "2026-09-07T12:00:00Z", "release_date": "Sep 7, 2026", "formatted_launched_ago": "Sep 7, 2026 (Updated 6d ago)", "launch_score": 92.9, "freshness_decay": 0.95, "integrity_multiplier": 1.05, "composite_velocity": 92.6, "cohort": "latest", "metrics": { "github_stars": 21000, "huggingface_downloads": 1950000, "benchmark_mats": "Function Calling Accuracy 94.0% | Inference Speed 180t/s", "throughput_tokens_sec": 180 }, "actions": { "try_url": "https://ai.google.dev/gemma", "github_url": "https://github.com/google-deepmind/gemma", "huggingface_url": "https://huggingface.co/google/gemma-2-9b-it", "paper_or_announcement_url": "https://deepmind.google" } }, { "id": "rel-o-66", "rank": 66, "name": "InternLM 3 20B 1M Context Reasoning", "provider": "Shanghai AI Lab", "maker_handle": "@InternLM", "slug": "internlm-3-20b", "tagline": "Open-weights reasoning engine with native 1M context window and rigorous mathematical CoT verification for research labs.", "category": "Open-Weights & MoE Engines", "subview_id": "open-weights", "architecture": "Dense Transformer with Long-Context Attention", "parameter_count": "20.4B", "context_window": "1M tokens", "license": "Apache 2.0", "timeframe": "monthly", "launched_at": "2026-08-27T10:00:00Z", "release_date": "Aug 27, 2026", "formatted_launched_ago": "Aug 27, 2026 (Updated 6d ago)", "launch_score": 92.5, "freshness_decay": 0.93, "integrity_multiplier": 1.05, "composite_velocity": 92.2, "cohort": "latest", "metrics": { "github_stars": 17200, "huggingface_downloads": 410000, "benchmark_mats": "1M Context Needle 99.6% | MathBench 78.4%", "throughput_tokens_sec": 110 }, "actions": { "try_url": "https://internlm.intern-ai.org.cn", "github_url": "https://github.com/InternLM/InternLM", "huggingface_url": "https://huggingface.co/internlm", "paper_or_announcement_url": "https://github.com/InternLM" } }, { "id": "rel-o-67", "rank": 67, "name": "Granite 3.1 8B Enterprise Instruct", "provider": "IBM Research", "maker_handle": "@IBMResearch", "slug": "granite-3-1-8b", "tagline": "Enterprise open-weights model fine-tuned for enterprise compliance, SQL queries, zero corporate data contamination, and IP indemnity.", "category": "Open-Weights & MoE Engines", "subview_id": "open-weights", "architecture": "Dense Enterprise Decoder Transformer", "parameter_count": "8.2B", "context_window": "128k tokens", "license": "Apache 2.0 (Full Commercial Indemnity)", "timeframe": "weekly", "launched_at": "2026-09-14T14:00:00Z", "release_date": "Sep 14, 2026", "formatted_launched_ago": "Sep 14, 2026 (Updated 4d ago)", "launch_score": 92, "freshness_decay": 0.96, "integrity_multiplier": 1.05, "composite_velocity": 91.8, "cohort": "latest", "metrics": { "github_stars": 8400, "huggingface_downloads": 580000, "benchmark_mats": "Safety & IP Indemnity 100% | Enterprise Tool Calling 91.2%", "throughput_tokens_sec": 155 }, "actions": { "try_url": "https://www.ibm.com/granite", "github_url": "https://github.com/ibm-granite", "huggingface_url": "https://huggingface.co/ibm-granite", "paper_or_announcement_url": "https://www.ibm.com/blog" } }, { "id": "rel-o-68", "rank": 68, "name": "Phi-4 Mini 3.8B Synthetic Reasoning", "provider": "Microsoft Research", "maker_handle": "@Microsoft", "slug": "phi-4-mini-3-8b", "tagline": "State-of-the-art small language model trained on synthetic textbooks and reasoning trees, punching far above its parameter weight in math and logic.", "category": "Open-Weights & MoE Engines", "subview_id": "open-weights", "architecture": "Synthetic-Trained Dense Small Transformer", "parameter_count": "3.8B", "context_window": "128k tokens", "license": "MIT License", "timeframe": "monthly", "launched_at": "2026-08-30T16:00:00Z", "release_date": "Aug 30, 2026", "formatted_launched_ago": "Aug 30, 2026 (Updated 5d ago)", "launch_score": 91.6, "freshness_decay": 0.94, "integrity_multiplier": 1.05, "composite_velocity": 91.4, "cohort": "latest", "metrics": { "github_stars": 28000, "huggingface_downloads": 1650000, "benchmark_mats": "MMLU 70.8% | GSM8K 82.4% | Memory 2.8GB", "throughput_tokens_sec": 220 }, "actions": { "try_url": "https://azure.microsoft.com/en-us/products/phi", "huggingface_url": "https://huggingface.co/microsoft/Phi-3-mini-4k-instruct", "paper_or_announcement_url": "https://www.microsoft.com/research" } }, { "id": "rel-m-69", "rank": 69, "name": "Eleven v4 Turbo Low-Latency Voice Engine", "provider": "ElevenLabs", "maker_handle": "@elevenlabsio", "slug": "eleven-v4-turbo", "tagline": "Sub-80ms low-latency expressive real-time voice synthesis model engineered for conversational AI agents, dynamic emotions, and multilingual inflection.", "category": "Multimodal, Video & Speech", "subview_id": "media", "architecture": "Streaming Neural Audio Codec & Acoustic Transformer", "parameter_count": "Audio Foundation Model", "context_window": "Streaming Audio Buffer", "license": "Commercial API & Voice Library", "timeframe": "daily", "launched_at": "2026-09-24T14:00:00Z", "release_date": "Sep 24, 2026", "formatted_launched_ago": "Sep 24, 2026 (Updated 1d ago)", "launch_score": 99.2, "freshness_decay": 0.98, "integrity_multiplier": 1.15, "composite_velocity": 99, "cohort": "latest", "metrics": { "github_stars": 18500, "huggingface_downloads": 1200000, "benchmark_mats": "TTFA (Time to First Audio) 78ms | Prosody Naturalness 98.4%", "throughput_tokens_sec": 320 }, "actions": { "try_url": "https://elevenlabs.io", "paper_or_announcement_url": "https://elevenlabs.io/blog" } }, { "id": "rel-m-70", "rank": 70, "name": "Gemini 3.8 Flash Live Voice-to-Voice Stream", "provider": "Google DeepMind", "maker_handle": "@GoogleDeepMind", "slug": "gemini-3-8-flash-live", "tagline": "Native bidirectional voice-to-voice stream with reasoning pauses, emotional modulation, and background audio suppression.", "category": "Multimodal, Video & Speech", "subview_id": "media", "architecture": "Multimodal Streaming Speech-to-Speech Transformer", "parameter_count": "Undisclosed Dense", "context_window": "1M tokens", "license": "Proprietary Commercial API", "timeframe": "weekly", "launched_at": "2026-09-23T16:00:00Z", "release_date": "Sep 23, 2026", "formatted_launched_ago": "Sep 23, 2026 (Updated 2d ago)", "launch_score": 98.7, "freshness_decay": 0.98, "integrity_multiplier": 1.14, "composite_velocity": 98.5, "cohort": "latest", "metrics": { "github_stars": 26000, "huggingface_downloads": 890000, "benchmark_mats": "Bidirectional Speech Latency 110ms | Interruptibility 99.2%", "throughput_tokens_sec": 140 }, "actions": { "try_url": "https://aistudio.google.com", "paper_or_announcement_url": "https://deepmind.google/technologies/gemini" } }, { "id": "rel-m-71", "rank": 71, "name": "Muse Video Physical Consistency Engine", "provider": "Meta Superintelligence Labs", "maker_handle": "@Meta", "slug": "muse-video", "tagline": "Tool-assisted generative video engine enforcing 3D physical consistency, optical flow permanence, and dynamic camera trajectory control.", "category": "Multimodal, Video & Speech", "subview_id": "media", "architecture": "3D Spatial-Temporal Diffusion Transformer", "parameter_count": "Video Foundation Model", "context_window": "Temporal Video Frames", "license": "Research Preview & Commercial API", "timeframe": "monthly", "launched_at": "2026-07-19T17:00:00Z", "release_date": "Jul 19, 2026", "formatted_launched_ago": "Jul 19, 2026 (Updated 2w ago)", "launch_score": 98.1, "freshness_decay": 0.94, "integrity_multiplier": 1.12, "composite_velocity": 97.8, "cohort": "latest", "metrics": { "github_stars": 31000, "huggingface_downloads": 620000, "benchmark_mats": "Physical Law Adherence 94.6% | 4K 60fps Native Rendering", "throughput_tokens_sec": 45 }, "actions": { "try_url": "https://ai.meta.com/research", "paper_or_announcement_url": "https://about.fb.com" } }, { "id": "rel-m-72", "rank": 72, "name": "Kling 3.0 Cinematic Multi-Shot Video", "provider": "Kuaishou", "maker_handle": "@Kling_ai", "slug": "kling-3-0", "tagline": "Multi-shot character permanence, director camera control, and physical simulation delivering 1080p 60fps cinematic sequences.", "category": "Multimodal, Video & Speech", "subview_id": "media", "architecture": "Diffusion Transformer with 3D VAE Latent Compression", "parameter_count": "Video Foundation Model", "context_window": "Multi-Shot Sequence Memory", "license": "Commercial Cloud Service", "timeframe": "monthly", "launched_at": "2026-08-28T10:00:00Z", "release_date": "Aug 28, 2026", "formatted_launched_ago": "Aug 28, 2026 (Updated 5d ago)", "launch_score": 97.6, "freshness_decay": 0.94, "integrity_multiplier": 1.1, "composite_velocity": 97.3, "cohort": "latest", "metrics": { "github_stars": 18500, "huggingface_downloads": 740000, "benchmark_mats": "Character Identity Preservation 96.8% | Camera Trajectory Accuracy 95.1%", "throughput_tokens_sec": 32 }, "actions": { "try_url": "https://klingai.com", "paper_or_announcement_url": "https://klingai.com/about" } }, { "id": "rel-m-73", "rank": 73, "name": "Suno v5 Multi-Track Stem Music Engine", "provider": "Suno AI", "maker_handle": "@suno_ai", "slug": "suno-v5", "tagline": "Multi-track audio stems, lyrical syllable control, real-time vocal harmonization, and studio-grade mastering up to 5-minute song duration.", "category": "Multimodal, Video & Speech", "subview_id": "media", "architecture": "Multi-Track Acoustic Diffusion Transformer", "parameter_count": "Audio Synthesis Engine", "context_window": "5-Minute Musical Horizon", "license": "Commercial Cloud Platform", "timeframe": "monthly", "launched_at": "2026-08-30T16:00:00Z", "release_date": "Aug 30, 2026", "formatted_launched_ago": "Aug 30, 2026 (Updated 5d ago)", "launch_score": 97.1, "freshness_decay": 0.94, "integrity_multiplier": 1.1, "composite_velocity": 96.8, "cohort": "latest", "metrics": { "github_stars": 24000, "huggingface_downloads": 1400000, "benchmark_mats": "Stem Separation Fidelity 98.2% | Syllable Synchronization 97.4%", "throughput_tokens_sec": 180 }, "actions": { "try_url": "https://suno.com", "paper_or_announcement_url": "https://suno.com/about" } }, { "id": "rel-m-74", "rank": 74, "name": "Higgsfield Cinematic Ads Video Engine", "provider": "Higgsfield AI", "maker_handle": "@higgsfield_ai", "slug": "higgsfield-video", "tagline": "Creator favorite for cinematic hyper-stylized camera moves, commercial ads, and short-form video generation with custom motion presets.", "category": "Multimodal, Video & Speech", "subview_id": "media", "architecture": "Motion-Conditioned Video Diffusion Transformer", "parameter_count": "Video Synthesis Engine", "context_window": "Motion Primitive Buffer", "license": "Commercial Subscription", "timeframe": "weekly", "launched_at": "2026-09-16T17:00:00Z", "release_date": "Sep 16, 2026", "formatted_launched_ago": "Sep 16, 2026 (Updated 3d ago)", "launch_score": 96.5, "freshness_decay": 0.97, "integrity_multiplier": 1.08, "composite_velocity": 96.3, "cohort": "latest", "metrics": { "github_stars": 14200, "huggingface_downloads": 380000, "benchmark_mats": "Camera Motion Flow Smoothness 96.2% | Creator Adoption +340%", "throughput_tokens_sec": 40 }, "actions": { "try_url": "https://higgsfield.ai", "paper_or_announcement_url": "https://higgsfield.ai/press" } }, { "id": "rel-m-75", "rank": 75, "name": "Nano Banana 2 Hyper-Aesthetic Visual Diffusion", "provider": "Midjourney Research", "maker_handle": "@midjourney", "slug": "nano-banana-2", "tagline": "High-aesthetic diffusion model specialized for complex product renders, ray-traced lighting, photorealistic textures, and studio commercial sets.", "category": "Multimodal, Video & Speech", "subview_id": "media", "architecture": "Rectified Flow High-Density Latent Diffusion", "parameter_count": "Visual Foundation Model", "context_window": "Text & Visual Conditioning", "license": "Commercial Service", "timeframe": "weekly", "launched_at": "2026-09-19T14:00:00Z", "release_date": "Sep 19, 2026", "formatted_launched_ago": "Sep 19, 2026 (Updated 3d ago)", "launch_score": 96, "freshness_decay": 0.97, "integrity_multiplier": 1.08, "composite_velocity": 95.8, "cohort": "latest", "metrics": { "github_stars": 29000, "huggingface_downloads": 820000, "benchmark_mats": "Aesthetic Score 9.4/10 | Ray-Tracing Surface Accuracy 97.1%", "throughput_tokens_sec": 50 }, "actions": { "try_url": "https://midjourney.com", "paper_or_announcement_url": "https://midjourney.com" } }, { "id": "rel-m-76", "rank": 76, "name": "ChatGPT Image 2.0 In-Context Generation", "provider": "OpenAI", "maker_handle": "@OpenAI", "slug": "chatgpt-image-2-0", "tagline": "Native multimodal image generation and in-painting inside ChatGPT with pixel-perfect typography rendering and iterative conversational edits.", "category": "Multimodal, Video & Speech", "subview_id": "media", "architecture": "Autoregressive Multimodal Token Diffusion", "parameter_count": "Undisclosed Multimodal", "context_window": "128k tokens", "license": "Proprietary Commercial Service", "timeframe": "weekly", "launched_at": "2026-09-21T16:00:00Z", "release_date": "Sep 21, 2026", "formatted_launched_ago": "Sep 21, 2026 (Updated 2d ago)", "launch_score": 95.7, "freshness_decay": 0.98, "integrity_multiplier": 1.08, "composite_velocity": 95.5, "cohort": "latest", "metrics": { "github_stars": 32000, "huggingface_downloads": 1450000, "benchmark_mats": "Prompt Text Spelled Correctly 96.4% | In-Context Modification 93.8%", "throughput_tokens_sec": 65 }, "actions": { "try_url": "https://chatgpt.com", "paper_or_announcement_url": "https://openai.com/index" } }, { "id": "rel-m-77", "rank": 77, "name": "KlingAI Pro Video Production Suite", "provider": "Kling AI", "maker_handle": "@Kling_ai", "slug": "klingai-pro-suite", "tagline": "Professional web suite combining character anchor keyframing, multi-camera storyboard orchestration, and commercial 4K render queues.", "category": "Multimodal, Video & Speech", "subview_id": "media", "architecture": "Video Generation Studio & Workflow Runner", "parameter_count": "Studio Suite", "context_window": "Storyboard State Buffer", "license": "Commercial Subscription", "timeframe": "weekly", "launched_at": "2026-09-25T16:00:00Z", "release_date": "Sep 25, 2026", "formatted_launched_ago": "Sep 25, 2026 (Updated 1d ago)", "launch_score": 95.2, "freshness_decay": 0.99, "integrity_multiplier": 1.06, "composite_velocity": 95, "cohort": "latest", "metrics": { "github_stars": 12500, "huggingface_downloads": 410000, "benchmark_mats": "Storyboard Sequence Consistency 95.2% | Render Latency -40%", "throughput_tokens_sec": 35 }, "actions": { "try_url": "https://klingai.com", "paper_or_announcement_url": "https://klingai.com" } }, { "id": "rel-m-78", "rank": 78, "name": "Udio v2.0 4-Minute Full Song Synthesis", "provider": "Udio Music", "maker_handle": "@udiomusic", "slug": "udio-v2-0", "tagline": "4-minute continuous song synthesis with stem separation, chord progression editing, and fine-grained lyrical syllabic timing.", "category": "Multimodal, Video & Speech", "subview_id": "media", "architecture": "Autoregressive Audio Codec & Diffusion Refiner", "parameter_count": "Audio Foundation Model", "context_window": "4-Minute Audio Latent", "license": "Commercial Subscription", "timeframe": "weekly", "launched_at": "2026-09-05T15:00:00Z", "release_date": "Sep 5, 2026", "formatted_launched_ago": "Sep 5, 2026 (Updated 5d ago)", "launch_score": 94.8, "freshness_decay": 0.95, "integrity_multiplier": 1.06, "composite_velocity": 94.5, "cohort": "latest", "metrics": { "github_stars": 16000, "huggingface_downloads": 780000, "benchmark_mats": "Musical Coherence 97.1% | Genre Versatility Index 95.4%", "throughput_tokens_sec": 140 }, "actions": { "try_url": "https://udio.com", "paper_or_announcement_url": "https://udio.com/about" } }, { "id": "rel-m-79", "rank": 79, "name": "FLUX.1 Pro Ultra 2K Resolution Flow", "provider": "Black Forest Labs", "maker_handle": "@bfl_ml", "slug": "flux-1-pro-ultra", "tagline": "2K native resolution rectified flow visual foundation model simulating raw CMOS sensor noise and high dynamic range optical bokeh.", "category": "Multimodal, Video & Speech", "subview_id": "media", "architecture": "Multimodal Diffusion Transformer (MMDiT)", "parameter_count": "16B", "context_window": "512 text tokens", "license": "Commercial API & Open Research Weights", "timeframe": "weekly", "launched_at": "2026-09-12T16:00:00Z", "release_date": "Sep 12, 2026", "formatted_launched_ago": "Sep 12, 2026 (Updated 4d ago)", "launch_score": 94.4, "freshness_decay": 0.96, "integrity_multiplier": 1.06, "composite_velocity": 94.1, "cohort": "latest", "metrics": { "github_stars": 32000, "huggingface_downloads": 4800000, "benchmark_mats": "GenEval 0.89 | Photorealism Rating 96.8%", "throughput_tokens_sec": 42 }, "actions": { "try_url": "https://blackforestlabs.ai", "github_url": "https://github.com/black-forest-labs/flux", "huggingface_url": "https://huggingface.co/black-forest-labs", "paper_or_announcement_url": "https://blackforestlabs.ai" } }, { "id": "rel-m-80", "rank": 80, "name": "Runway Gen-4 Alpha Interactive Physics", "provider": "Runway", "maker_handle": "@runwayml", "slug": "runway-gen-4-alpha", "tagline": "Generative video world engine with interactive camera physics, gravity simulation, dynamic light bounces, and keyframe interpolation.", "category": "Multimodal, Video & Speech", "subview_id": "media", "architecture": "World Simulation Spatial Diffusion Transformer", "parameter_count": "Video World Model", "context_window": "3D World Latent State", "license": "Commercial Creative Suite", "timeframe": "monthly", "launched_at": "2026-08-18T15:00:00Z", "release_date": "Aug 18, 2026", "formatted_launched_ago": "Aug 18, 2026 (Updated 6d ago)", "launch_score": 93.9, "freshness_decay": 0.92, "integrity_multiplier": 1.06, "composite_velocity": 93.5, "cohort": "latest", "metrics": { "github_stars": 21000, "huggingface_downloads": 920000, "benchmark_mats": "Physics Simulation Accuracy 93.4% | Real-Time Latency <3s", "throughput_tokens_sec": 40 }, "actions": { "try_url": "https://runwayml.com", "paper_or_announcement_url": "https://runwayml.com/news" } }, { "id": "rel-m-81", "rank": 81, "name": "Pika 2.5 Physics Effects Video Generator", "provider": "Pika Labs", "maker_handle": "@pika_labs", "slug": "pika-2-5", "tagline": "Physics effect generation engine enabling users to melt, explode, crush, inflate, and squish visual objects with realistic material elasticity.", "category": "Multimodal, Video & Speech", "subview_id": "media", "architecture": "Material-Aware Video Diffusion Model", "parameter_count": "Video Synthesis Engine", "context_window": "Material State Buffer", "license": "Commercial Subscription", "timeframe": "weekly", "launched_at": "2026-09-09T17:00:00Z", "release_date": "Sep 9, 2026", "formatted_launched_ago": "Sep 9, 2026 (Updated 5d ago)", "launch_score": 93.4, "freshness_decay": 0.95, "integrity_multiplier": 1.05, "composite_velocity": 93, "cohort": "latest", "metrics": { "github_stars": 14500, "huggingface_downloads": 510000, "benchmark_mats": "Material Deform Fidelity 94.6% | Short-Form Viral Share +410%", "throughput_tokens_sec": 45 }, "actions": { "try_url": "https://pika.art", "paper_or_announcement_url": "https://pika.art/blog" } }, { "id": "rel-m-82", "rank": 82, "name": "Whisper large-v3-turbo Multilingual Speech", "provider": "OpenAI / Hugging Face", "maker_handle": "@OpenAI", "slug": "whisper-large-v3-turbo", "tagline": "Fine-tuned speech-to-text model achieving 8x speedup over large-v3 with minimal word error rate loss across 99 languages.", "category": "Multimodal, Video & Speech", "subview_id": "media", "architecture": "Encoder-Decoder Speech Transformer (4 decoder layers)", "parameter_count": "809M", "context_window": "30s audio chunks", "license": "MIT License", "timeframe": "weekly", "launched_at": "2026-09-20T15:00:00Z", "release_date": "Sep 20, 2026", "formatted_launched_ago": "Sep 20, 2026 (Updated 3d ago)", "launch_score": 92.9, "freshness_decay": 0.97, "integrity_multiplier": 1.05, "composite_velocity": 92.6, "cohort": "latest", "metrics": { "github_stars": 71000, "huggingface_downloads": 2400000, "benchmark_mats": "CommonVoice WER 7.1% | Speedup 8.1x", "throughput_tokens_sec": 380 }, "actions": { "try_url": "https://huggingface.co/openai/whisper-large-v3-turbo", "github_url": "https://github.com/openai/whisper", "huggingface_url": "https://huggingface.co/openai/whisper-large-v3-turbo", "paper_or_announcement_url": "https://github.com/openai/whisper/discussions/2330" } }, { "id": "rel-m-83", "rank": 83, "name": "Cartesia Sonic 2.0 Streaming Voice", "provider": "Cartesia AI", "maker_handle": "@CartesiaAI", "slug": "cartesia-sonic-2-0", "tagline": "Ultra-low latency streaming voice model running on state space architecture with emotional prosody control and sub-90ms first audio packet.", "category": "Multimodal, Video & Speech", "subview_id": "media", "architecture": "State Space Acoustic Streaming Model", "parameter_count": "Speech Synthesis Engine", "context_window": "Continuous Streaming Audio", "license": "Commercial API & SDK", "timeframe": "weekly", "launched_at": "2026-09-14T15:00:00Z", "release_date": "Sep 14, 2026", "formatted_launched_ago": "Sep 14, 2026 (Updated 4d ago)", "launch_score": 92.4, "freshness_decay": 0.96, "integrity_multiplier": 1.05, "composite_velocity": 92.1, "cohort": "latest", "metrics": { "github_stars": 7800, "huggingface_downloads": 320000, "benchmark_mats": "TTFA 82ms | Naturalness MOS 4.62", "throughput_tokens_sec": 240 }, "actions": { "try_url": "https://cartesia.ai", "paper_or_announcement_url": "https://cartesia.ai/blog" } }, { "id": "rel-m-84", "rank": 84, "name": "Mistral Pixtral Large 124B Multimodal", "provider": "Mistral AI", "maker_handle": "@MistralAI", "slug": "mistral-pixtral-large", "tagline": "Frontier open-weights 124B multimodal model designed for chart comprehension, high-resolution document parsing, and visual code synthesis.", "category": "Multimodal, Video & Speech", "subview_id": "media", "architecture": "Multimodal Decoder Transformer with 400M Vision Encoder", "parameter_count": "124B", "context_window": "128k tokens", "license": "Mistral Research License", "timeframe": "weekly", "launched_at": "2026-09-17T10:00:00Z", "release_date": "Sep 17, 2026", "formatted_launched_ago": "Sep 17, 2026 (Updated 3d ago)", "launch_score": 92, "freshness_decay": 0.97, "integrity_multiplier": 1.05, "composite_velocity": 91.7, "cohort": "latest", "metrics": { "github_stars": 26000, "huggingface_downloads": 560000, "benchmark_mats": "MMMU 69.4% | DocVQA 93.8% | ChartQA 88.2%", "throughput_tokens_sec": 48 }, "actions": { "try_url": "https://chat.mistral.ai", "github_url": "https://github.com/mistralai/mistral-common", "huggingface_url": "https://huggingface.co/mistralai/Pixtral-Large-Instruct-2411", "paper_or_announcement_url": "https://mistral.ai/news/pixtral-large" } }, { "id": "rel-i-85", "rank": 85, "name": "Paperclip Multi-Agent Hierarchy & Budget Manager", "provider": "Paperclip Open Source", "maker_handle": "@PaperclipAI", "slug": "paperclip-multiagent", "tagline": "Multi-agent hierarchy framework with explicit task delegation contracts, rate limits, token burn alerts, and deterministic loop termination.", "category": "Agent Memory, Orchestration & Infra", "subview_id": "infra", "architecture": "Hierarchical Agent Governor & Token Rate Limiter", "parameter_count": "Orchestration Library", "context_window": "Contract State Graph", "license": "MIT License", "timeframe": "weekly", "launched_at": "2026-09-22T14:00:00Z", "release_date": "Sep 22, 2026", "formatted_launched_ago": "Sep 22, 2026 (Updated 2d ago)", "launch_score": 99, "freshness_decay": 0.98, "integrity_multiplier": 1.15, "composite_velocity": 98.8, "cohort": "latest", "metrics": { "github_stars": 28400, "huggingface_downloads": 340000, "benchmark_mats": "Zero Runaway Loops | Cost Containment 99.4%", "throughput_tokens_sec": 140 }, "actions": { "try_url": "https://github.com/paperclip-ai/paperclip", "github_url": "https://github.com/paperclip-ai/paperclip", "paper_or_announcement_url": "https://github.com/paperclip-ai/paperclip/releases" } }, { "id": "rel-i-86", "rank": 86, "name": "Hindsight Long-Term Semantic Memory Engine", "provider": "Hindsight Open Source", "maker_handle": "@HindsightAI", "slug": "hindsight-memory", "tagline": "Long-term semantic memory and cross-session persistence engine for LLM agents with episodic vector recall and temporal facts decay.", "category": "Agent Memory, Orchestration & Infra", "subview_id": "infra", "architecture": "Episodic Semantic Memory Layer with Knowledge Graph Pruning", "parameter_count": "Memory Infrastructure", "context_window": "Continuous Cross-Session State", "license": "Apache 2.0", "timeframe": "weekly", "launched_at": "2026-09-20T16:00:00Z", "release_date": "Sep 20, 2026", "formatted_launched_ago": "Sep 20, 2026 (Updated 3d ago)", "launch_score": 98.4, "freshness_decay": 0.97, "integrity_multiplier": 1.12, "composite_velocity": 98.2, "cohort": "latest", "metrics": { "github_stars": 21500, "huggingface_downloads": 290000, "benchmark_mats": "Recall Accuracy 98.6% | Query Latency <14ms", "throughput_tokens_sec": 180 }, "actions": { "try_url": "https://github.com/hindsight-ai/hindsight", "github_url": "https://github.com/hindsight-ai/hindsight", "paper_or_announcement_url": "https://hindsight-ai.github.io" } }, { "id": "rel-i-87", "rank": 87, "name": "OpenRig Local WASM & Docker Agent Harness", "provider": "OpenRig", "maker_handle": "@OpenRigAI", "slug": "openrig-harness", "tagline": "Lightweight local LLM execution harness running computer-use agents in isolated WASM/Docker sandboxes with zero host access permissions.", "category": "Agent Memory, Orchestration & Infra", "subview_id": "infra", "architecture": "WASM Sandboxed Container Controller", "parameter_count": "Execution Runtime", "context_window": "Isolated Sandbox FS", "license": "MIT License", "timeframe": "monthly", "launched_at": "2026-08-25T14:00:00Z", "release_date": "Aug 25, 2026", "formatted_launched_ago": "Aug 25, 2026 (Updated 5d ago)", "launch_score": 97.6, "freshness_decay": 0.94, "integrity_multiplier": 1.1, "composite_velocity": 97.3, "cohort": "latest", "metrics": { "github_stars": 16800, "huggingface_downloads": 140000, "benchmark_mats": "WASM Cold Start <18ms | Zero Privilege Escalation", "throughput_tokens_sec": 160 }, "actions": { "try_url": "https://github.com/openrig-ai/openrig", "github_url": "https://github.com/openrig-ai/openrig", "paper_or_announcement_url": "https://openrig.dev" } }, { "id": "rel-i-88", "rank": 88, "name": "Pipecat Real-Time Voice Agent Receptionist", "provider": "Daily / Pipecat", "maker_handle": "@daily_co", "slug": "pipecat-voice-agent", "tagline": "Open-source framework for building real-time voice agents that answer inbound phone calls, schedule appointments, and book calendar invites.", "category": "Agent Memory, Orchestration & Infra", "subview_id": "infra", "architecture": "WebRTC Pipeline & Multi-Modal Streaming Orchestrator", "parameter_count": "Framework SDK", "context_window": "Streaming Audio Buffer", "license": "BSD-2-Clause", "timeframe": "weekly", "launched_at": "2026-09-25T15:00:00Z", "release_date": "Sep 25, 2026", "formatted_launched_ago": "Sep 25, 2026 (Updated 1d ago)", "launch_score": 97.2, "freshness_decay": 0.99, "integrity_multiplier": 1.1, "composite_velocity": 97.1, "cohort": "latest", "metrics": { "github_stars": 19200, "huggingface_downloads": 380000, "benchmark_mats": "End-to-End Phone Latency <190ms | WebRTC 99.9% Uptime", "throughput_tokens_sec": 220 }, "actions": { "try_url": "https://pipecat.ai", "github_url": "https://github.com/pipecat-ai/pipecat", "paper_or_announcement_url": "https://pipecat.ai/blog" } }, { "id": "rel-i-89", "rank": 89, "name": "Postiz Autonomous Social Content Scheduling", "provider": "Postiz Open Source", "maker_handle": "@postiz_ai", "slug": "postiz-social", "tagline": "Open-source autonomous social media scheduler scheduling a full month of AI-generated content across Instagram, LinkedIn, Threads, and X.", "category": "Agent Memory, Orchestration & Infra", "subview_id": "infra", "architecture": "Automated Social Media Ingestion & Multi-Platform Publisher", "parameter_count": "Full-Stack Application", "context_window": "Editorial Calendar Buffer", "license": "GPL-3.0", "timeframe": "weekly", "launched_at": "2026-09-23T12:00:00Z", "release_date": "Sep 23, 2026", "formatted_launched_ago": "Sep 23, 2026 (Updated 2d ago)", "launch_score": 96.8, "freshness_decay": 0.98, "integrity_multiplier": 1.08, "composite_velocity": 96.6, "cohort": "latest", "metrics": { "github_stars": 22400, "huggingface_downloads": 180000, "benchmark_mats": "Multi-Channel Publishing Uptime 99.8% | Viral Engagement +280%", "throughput_tokens_sec": 90 }, "actions": { "try_url": "https://postiz.com", "github_url": "https://github.com/gitroomhq/postiz-app", "paper_or_announcement_url": "https://postiz.com/docs" } }, { "id": "rel-i-90", "rank": 90, "name": "OpenSEO Autonomous Search Intent Engine", "provider": "OpenSEO", "maker_handle": "@OpenSEO_AI", "slug": "openseo-engine", "tagline": "Free autonomous SEO intelligence tool that analyzes search intent, monitors competitor rankings, and audits technical site crawl issues.", "category": "Agent Memory, Orchestration & Infra", "subview_id": "infra", "architecture": "Autonomous Crawler & SERP Telemetry Analyzer", "parameter_count": "Analytics Platform", "context_window": "SERP Graph Memory", "license": "MIT License (Open Source)", "timeframe": "weekly", "launched_at": "2026-09-21T11:00:00Z", "release_date": "Sep 21, 2026", "formatted_launched_ago": "Sep 21, 2026 (Updated 3d ago)", "launch_score": 96.3, "freshness_decay": 0.98, "integrity_multiplier": 1.08, "composite_velocity": 96.1, "cohort": "latest", "metrics": { "github_stars": 16500, "huggingface_downloads": 140000, "benchmark_mats": "Audit Speed 1,000 URLs/min | SERP Intent Correlation 94.8%", "throughput_tokens_sec": 150 }, "actions": { "try_url": "https://openseo.dev", "github_url": "https://github.com/openseo-dev/openseo", "paper_or_announcement_url": "https://openseo.dev/blog" } }, { "id": "rel-i-91", "rank": 91, "name": "AI Group Call Multi-Persona Debate Engine", "provider": "AI Group Call Labs", "maker_handle": "@AIGroupCall", "slug": "ai-group-call", "tagline": "Simulates a phone call between 6 customized AI expert personas debating ideas, stress-testing venture hypotheses, and outputting synchronized transcripts.", "category": "Agent Memory, Orchestration & Infra", "subview_id": "infra", "architecture": "Multi-Persona Round-Robin Conversation Coordinator", "parameter_count": "Multi-Agent Simulator", "context_window": "Debate Transcript Buffer", "license": "Commercial Cloud Service", "timeframe": "weekly", "launched_at": "2026-09-19T18:00:00Z", "release_date": "Sep 19, 2026", "formatted_launched_ago": "Sep 19, 2026 (Updated 3d ago)", "launch_score": 95.8, "freshness_decay": 0.97, "integrity_multiplier": 1.08, "composite_velocity": 95.6, "cohort": "latest", "metrics": { "github_stars": 14800, "huggingface_downloads": 210000, "benchmark_mats": "Cross-Talk Elimination 98.4% | Hypothesis Stress-Test Score 92.6%", "throughput_tokens_sec": 110 }, "actions": { "try_url": "https://aigroupcall.com", "paper_or_announcement_url": "https://aigroupcall.com/demo" } }, { "id": "rel-i-92", "rank": 92, "name": "AI Job Search Autonomous Career Agent", "provider": "AI Job Search", "maker_handle": "@AIJobSearchAI", "slug": "ai-job-search", "tagline": "Autonomous career agent that hunts job openings across 40 boards, dynamically tailors resumes, writes personalized cover letters, and tracks interviews.", "category": "Agent Memory, Orchestration & Infra", "subview_id": "infra", "architecture": "Career Workflow Agent with Headless Form Automation", "parameter_count": "Agent Service", "context_window": "Candidate Profile Graph", "license": "Free Tier & Premium Service", "timeframe": "weekly", "launched_at": "2026-09-24T12:00:00Z", "release_date": "Sep 24, 2026", "formatted_launched_ago": "Sep 24, 2026 (Updated 2d ago)", "launch_score": 95.4, "freshness_decay": 0.98, "integrity_multiplier": 1.06, "composite_velocity": 95.2, "cohort": "latest", "metrics": { "github_stars": 31000, "huggingface_downloads": 480000, "benchmark_mats": "ATS Keyword Match 96.8% | Interview Conversion +210%", "throughput_tokens_sec": 85 }, "actions": { "try_url": "https://aijobsearch.io", "paper_or_announcement_url": "https://aijobsearch.io/about" } }, { "id": "rel-i-93", "rank": 93, "name": "Verda AI Cloud $189M Frontier GPU Mesh", "provider": "Verda Cloud", "maker_handle": "@VerdaCloud", "slug": "verda-ai-cloud", "tagline": "Ultra-low latency European GPU cloud infrastructure engineered for MiMo and MoE serving, raised $189M during peak September model race.", "category": "Agent Memory, Orchestration & Infra", "subview_id": "infra", "architecture": "High-Density GPU Interconnect & RDMA Cluster", "parameter_count": "Cloud Infrastructure", "context_window": "N/A", "license": "Enterprise Cloud Service", "timeframe": "weekly", "launched_at": "2026-09-22T09:00:00Z", "release_date": "Sep 22, 2026", "formatted_launched_ago": "Sep 22, 2026 (Updated 2d ago)", "launch_score": 95, "freshness_decay": 0.98, "integrity_multiplier": 1.08, "composite_velocity": 94.8, "cohort": "latest", "metrics": { "github_stars": 8400, "huggingface_downloads": 160000, "benchmark_mats": "RDMA Latency <1.2μs | 100% Green Energy Powered", "throughput_tokens_sec": 4200 }, "actions": { "try_url": "https://verda.ai", "paper_or_announcement_url": "https://verda.ai/press" } }, { "id": "rel-i-94", "rank": 94, "name": "Descript AI Video & Voice Suite 2026", "provider": "Descript", "maker_handle": "@DescriptApp", "slug": "descript-ai-suite", "tagline": "Text-based video and audio editor with AI voice cloning, eye-contact redirection, automatic filler word removal, and automated chaptering.", "category": "Agent Memory, Orchestration & Infra", "subview_id": "infra", "architecture": "Text-Driven Audio/Video Editing Transformer", "parameter_count": "Creative Desktop Suite", "context_window": "Timeline Sequence Buffer", "license": "Commercial Subscription", "timeframe": "weekly", "launched_at": "2026-09-15T14:00:00Z", "release_date": "Sep 15, 2026", "formatted_launched_ago": "Sep 15, 2026 (Updated 4d ago)", "launch_score": 94.6, "freshness_decay": 0.97, "integrity_multiplier": 1.06, "composite_velocity": 94.3, "cohort": "latest", "metrics": { "github_stars": 18000, "huggingface_downloads": 1950000, "benchmark_mats": "Filler Word Stripping Precision 99.1% | Eye-Contact Realism 97.4%", "throughput_tokens_sec": 140 }, "actions": { "try_url": "https://descript.com", "paper_or_announcement_url": "https://descript.com/blog" } }, { "id": "rel-i-95", "rank": 95, "name": "Topicfinder.ai Viral Research Engine", "provider": "Topicfinder", "maker_handle": "@TopicfinderAI", "slug": "topicfinder-ai", "tagline": "Algorithmic topic discovery engine mining high-velocity content ideas across YouTube, Reddit, and TikTok for AI creators.", "category": "Agent Memory, Orchestration & Infra", "subview_id": "infra", "architecture": "Social Virality Clustering & Trend Extrapolation Model", "parameter_count": "Analytics Engine", "context_window": "Social Data Lake", "license": "Commercial Creator Tool", "timeframe": "weekly", "launched_at": "2026-09-18T15:00:00Z", "release_date": "Sep 18, 2026", "formatted_launched_ago": "Sep 18, 2026 (Updated 3d ago)", "launch_score": 94.2, "freshness_decay": 0.97, "integrity_multiplier": 1.06, "composite_velocity": 94, "cohort": "latest", "metrics": { "github_stars": 11500, "huggingface_downloads": 240000, "benchmark_mats": "Trend Early Detection +48h Before Peak | Virality Score 94.1%", "throughput_tokens_sec": 95 }, "actions": { "try_url": "https://topicfinder.ai", "paper_or_announcement_url": "https://topicfinder.ai/blog" } }, { "id": "rel-i-96", "rank": 96, "name": "NotebookLM Audio Deep Dive Overviews", "provider": "Google DeepMind", "maker_handle": "@GoogleDeepMind", "slug": "notebooklm-audio-overviews", "tagline": "Grounded multi-source audio synthesis engine turning dense research PDFs and datasets into realistic podcast discussions between two host personas.", "category": "Agent Memory, Orchestration & Infra", "subview_id": "infra", "architecture": "Grounded Dialogue Generator & Dual-Speaker Acoustic Synthesizer", "parameter_count": "Research Tool", "context_window": "Notebook Document Context (Up to 50 PDFs)", "license": "Free Research Service", "timeframe": "weekly", "launched_at": "2026-09-14T16:00:00Z", "release_date": "Sep 14, 2026", "formatted_launched_ago": "Sep 14, 2026 (Updated 4d ago)", "launch_score": 93.8, "freshness_decay": 0.96, "integrity_multiplier": 1.06, "composite_velocity": 93.5, "cohort": "latest", "metrics": { "github_stars": 44000, "huggingface_downloads": 3200000, "benchmark_mats": "Source Grounding 98.9% | Natural Conversational Inflection 97.6%", "throughput_tokens_sec": 120 }, "actions": { "try_url": "https://notebooklm.google.com", "paper_or_announcement_url": "https://blog.google/technology/ai" } }, { "id": "rel-i-97", "rank": 97, "name": "LangGraph 1.0 Production Multi-Agent Engine", "provider": "LangChain", "maker_handle": "@LangChainAI", "slug": "langgraph-1-0", "tagline": "Production-grade multi-agent cyclic graph framework with human-in-the-loop checkpoints, persistent time-travel state, and deterministic streaming.", "category": "Agent Memory, Orchestration & Infra", "subview_id": "infra", "architecture": "Cyclic State Graph Execution Engine", "parameter_count": "Framework Library", "context_window": "Checkpointed State Store", "license": "MIT License", "timeframe": "weekly", "launched_at": "2026-09-11T14:00:00Z", "release_date": "Sep 11, 2026", "formatted_launched_ago": "Sep 11, 2026 (Updated 5d ago)", "launch_score": 93.3, "freshness_decay": 0.96, "integrity_multiplier": 1.06, "composite_velocity": 93, "cohort": "latest", "metrics": { "github_stars": 96000, "huggingface_downloads": 4100000, "benchmark_mats": "Cyclic Graph Reliability 99.4% | Time-Travel Replay Speed <5ms", "throughput_tokens_sec": 180 }, "actions": { "try_url": "https://langchain.com/langgraph", "github_url": "https://github.com/langchain-ai/langgraph", "paper_or_announcement_url": "https://blog.langchain.dev" } }, { "id": "rel-i-98", "rank": 98, "name": "LlamaIndex Workflows 2.0 Event-Driven RAG", "provider": "LlamaIndex", "maker_handle": "@llama_index", "slug": "llamaindex-workflows-2", "tagline": "Event-driven orchestration framework for document ingestion, multi-tenant RAG, and autonomous tool agents with async event dispatching.", "category": "Agent Memory, Orchestration & Infra", "subview_id": "infra", "architecture": "Async Event-Driven Agent State Graph", "parameter_count": "Framework Library", "context_window": "Modular Index Graph", "license": "MIT License", "timeframe": "weekly", "launched_at": "2026-09-13T15:00:00Z", "release_date": "Sep 13, 2026", "formatted_launched_ago": "Sep 13, 2026 (Updated 4d ago)", "launch_score": 92.9, "freshness_decay": 0.96, "integrity_multiplier": 1.05, "composite_velocity": 92.6, "cohort": "latest", "metrics": { "github_stars": 37500, "huggingface_downloads": 2800000, "benchmark_mats": "RAG Ingestion Speed 3x | Async Step Concurrency 10,000+", "throughput_tokens_sec": 160 }, "actions": { "try_url": "https://llamaindex.ai", "github_url": "https://github.com/run-llama/llama_index", "paper_or_announcement_url": "https://www.llamaindex.ai/blog" } }, { "id": "rel-i-99", "rank": 99, "name": "DSPy 2.5 Algorithmic Prompt Compiler", "provider": "Stanford NLP", "maker_handle": "@StanfordNLP", "slug": "dspy-2-5", "tagline": "Algorithmic prompt compiler optimizing LM prompts and pipeline weights automatically against assertions without brittle manual prompting.", "category": "Agent Memory, Orchestration & Infra", "subview_id": "infra", "architecture": "Self-Optimizing LM Pipeline Compiler", "parameter_count": "Research Framework", "context_window": "Optimization Trajectory Buffer", "license": "MIT License", "timeframe": "weekly", "launched_at": "2026-09-10T15:00:00Z", "release_date": "Sep 10, 2026", "formatted_launched_ago": "Sep 10, 2026 (Updated 5d ago)", "launch_score": 92.4, "freshness_decay": 0.95, "integrity_multiplier": 1.06, "composite_velocity": 92.2, "cohort": "latest", "metrics": { "github_stars": 21800, "huggingface_downloads": 620000, "benchmark_mats": "Metric Improvement +18% over Hand-Crafted Prompts | Zero Human Tuning", "throughput_tokens_sec": 190 }, "actions": { "try_url": "https://dspy.ai", "github_url": "https://github.com/stanfordnlp/dspy", "paper_or_announcement_url": "https://dspy.ai" } }, { "id": "rel-i-100", "rank": 100, "name": "Unstructured.io v0.16 Multimodal Parser", "provider": "Unstructured Technologies", "maker_handle": "@unstructuredio", "slug": "unstructured-v0-16", "tagline": "High-throughput document transformation library parsing tables, scanned PDFs, images, and presentation decks into clean RAG chunks.", "category": "Agent Memory, Orchestration & Infra", "subview_id": "infra", "architecture": "Computer Vision & Layout Parser Pipeline", "parameter_count": "Data Ingestion Library", "context_window": "Document Chunks", "license": "Apache 2.0", "timeframe": "weekly", "launched_at": "2026-09-06T12:00:00Z", "release_date": "Sep 6, 2026", "formatted_launched_ago": "Sep 6, 2026 (Updated 6d ago)", "launch_score": 91.9, "freshness_decay": 0.95, "integrity_multiplier": 1.05, "composite_velocity": 91.7, "cohort": "latest", "metrics": { "github_stars": 12800, "huggingface_downloads": 1400000, "benchmark_mats": "Table Extraction Accuracy 95.8% | OCR Precision 98.1%", "throughput_tokens_sec": 210 }, "actions": { "try_url": "https://unstructured.io", "github_url": "https://github.com/Unstructured-IO/unstructured", "paper_or_announcement_url": "https://unstructured.io/blog" } } ], "historical_baseline": [ { "id": "rel-h-101", "rank": 101, "name": "DeepSeek-R1 (671B MoE & Distillations)", "provider": "DeepSeek AI", "maker_handle": "@deepseek_ai", "slug": "deepseek-r1-historical", "tagline": "Breakthrough open-weights reasoning model matching OpenAI o1 on AIME, MATH-500, and Codeforces with multi-head latent attention (January 2026 landmark).", "category": "Frontier Models & Flagships", "subview_id": "historical", "architecture": "Mixture-of-Experts (MoE) with Multi-Head Latent Attention (MLA)", "parameter_count": "671B (37B active)", "context_window": "128k tokens", "license": "MIT License (Fully Open)", "timeframe": "monthly", "launched_at": "2026-01-20T14:00:00Z", "release_date": "Jan 20, 2026", "formatted_launched_ago": "Jan 20, 2026 (Preserved Baseline)", "launch_score": 99.4, "freshness_decay": 0.72, "integrity_multiplier": 1.15, "composite_velocity": 82.3, "cohort": "historical", "metrics": { "github_stars": 96500, "huggingface_downloads": 3800000, "benchmark_mats": "AIME 79.8% | MATH-500 97.3% | SWE-bench 49.2%", "throughput_tokens_sec": 64, "aa_intelligence_index": 46.8 }, "actions": { "try_url": "https://chat.deepseek.com", "github_url": "https://github.com/deepseek-ai/DeepSeek-R1", "huggingface_url": "https://huggingface.co/deepseek-ai/DeepSeek-R1", "paper_or_announcement_url": "https://github.com/deepseek-ai/DeepSeek-R1/blob/main/DeepSeek_R1.pdf" } }, { "id": "rel-h-102", "rank": 102, "name": "Claude 3.5 Sonnet (Original October Release)", "provider": "Anthropic", "maker_handle": "@AnthropicAI", "slug": "claude-3-5-sonnet-original", "tagline": "Historical milestone: pioneer agentic model introducing native computer use and breaking the 49% mark on SWE-bench Verified.", "category": "Consumer Agents & Platforms", "subview_id": "historical", "architecture": "Autoregressive Transformer with Computer Use Tool API", "parameter_count": "Undisclosed Dense", "context_window": "200k tokens", "license": "Proprietary Commercial API", "timeframe": "monthly", "launched_at": "2024-10-22T14:00:00Z", "release_date": "Oct 22, 2024", "formatted_launched_ago": "Oct 22, 2024 (Preserved Baseline)", "launch_score": 98.7, "freshness_decay": 0.65, "integrity_multiplier": 1.12, "composite_velocity": 71.8, "cohort": "historical", "metrics": { "github_stars": 52000, "huggingface_downloads": 1400000, "benchmark_mats": "SWE-bench Verified 49.0% | TAU-bench 69.2%", "throughput_tokens_sec": 82, "aa_intelligence_index": 48.4 }, "actions": { "try_url": "https://claude.ai", "paper_or_announcement_url": "https://anthropic.com/news/3-5-models-and-computer-use" } }, { "id": "rel-h-103", "rank": 103, "name": "OpenAI o3-mini Reasoning Engine", "provider": "OpenAI", "maker_handle": "@OpenAI", "slug": "openai-o3-mini-historical", "tagline": "Cost-efficient frontier STEM reasoning model optimized for mathematical deduction, code generation, and complex API planning.", "category": "Frontier Models & Flagships", "subview_id": "historical", "architecture": "Reinforcement Learning CoT Transformer", "parameter_count": "Undisclosed Dense", "context_window": "200k tokens", "license": "Proprietary Commercial API", "timeframe": "monthly", "launched_at": "2025-01-31T18:00:00Z", "release_date": "Jan 31, 2025", "formatted_launched_ago": "Jan 31, 2025 (Preserved Baseline)", "launch_score": 97.9, "freshness_decay": 0.74, "integrity_multiplier": 1.1, "composite_velocity": 79.7, "cohort": "historical", "metrics": { "github_stars": 38000, "huggingface_downloads": 980000, "benchmark_mats": "AIME 87.3% | Codeforces 2175 Rating", "throughput_tokens_sec": 95 }, "actions": { "try_url": "https://chatgpt.com", "paper_or_announcement_url": "https://openai.com/index/openai-o1-system-card" } }, { "id": "rel-h-104", "rank": 104, "name": "Llama 3.3 70B Instruct (December 2024 Launch)", "provider": "Meta AI", "maker_handle": "@MetaAI", "slug": "llama-3-3-70b-historical", "tagline": "Historical milestone: dense 70B model matching previous generation 405B capabilities across MMLU and coding benchmarks.", "category": "Open-Weights & MoE Engines", "subview_id": "historical", "architecture": "Dense Auto-regressive Transformer (GQA 8-KV heads)", "parameter_count": "70.6B", "context_window": "128k tokens", "license": "Llama 3.3 Community License", "timeframe": "monthly", "launched_at": "2024-12-06T17:00:00Z", "release_date": "Dec 6, 2024", "formatted_launched_ago": "Dec 6, 2024 (Preserved Baseline)", "launch_score": 95.5, "freshness_decay": 0.68, "integrity_multiplier": 1.06, "composite_velocity": 68.8, "cohort": "historical", "metrics": { "github_stars": 84000, "huggingface_downloads": 4100000, "benchmark_mats": "MMLU 88.6% | MATH 73.1% | IFEval 89.4%", "throughput_tokens_sec": 52 }, "actions": { "try_url": "https://llama.meta.com", "github_url": "https://github.com/meta-llama/llama3", "huggingface_url": "https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct", "paper_or_announcement_url": "https://ai.meta.com/blog/llama-3-3-70b" } }, { "id": "rel-h-105", "rank": 105, "name": "SmolLM2 1.7B (November 2024 Initial Release)", "provider": "Hugging Face", "maker_handle": "@huggingface", "slug": "smollm2-historical", "tagline": "Initial baseline for ultra-compact on-device instruction modeling trained on 11T tokens for edge devices.", "category": "Open-Weights & MoE Engines", "subview_id": "historical", "architecture": "Compact Dense Transformer for Edge Devices", "parameter_count": "1.71B", "context_window": "8k tokens", "license": "Apache 2.0", "timeframe": "monthly", "launched_at": "2024-11-04T12:00:00Z", "release_date": "Nov 4, 2024", "formatted_launched_ago": "Nov 4, 2024 (Preserved Baseline)", "launch_score": 90.6, "freshness_decay": 0.66, "integrity_multiplier": 1.05, "composite_velocity": 62.8, "cohort": "historical", "metrics": { "github_stars": 18500, "huggingface_downloads": 2400000, "benchmark_mats": "MMLU 53.6% | GSM8K 65.2%", "throughput_tokens_sec": 180 }, "actions": { "try_url": "https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B-Instruct", "github_url": "https://github.com/huggingface/smollm", "huggingface_url": "https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B-Instruct", "paper_or_announcement_url": "https://huggingface.co/blog/smollm2" } }, { "id": "rel-h-106", "rank": 106, "name": "Whisper large-v3-turbo (October 2024 Initial Release)", "provider": "OpenAI", "maker_handle": "@OpenAI", "slug": "whisper-large-v3-turbo-historical", "tagline": "Historical milestone: pruning decoder layers from 32 to 4, demonstrating that speech transcription models can run 8x faster.", "category": "Multimodal, Video & Speech", "subview_id": "historical", "architecture": "Encoder-Decoder Speech Transformer", "parameter_count": "809M", "context_window": "30s audio chunks", "license": "MIT License", "timeframe": "monthly", "launched_at": "2024-10-01T15:00:00Z", "release_date": "Oct 1, 2024", "formatted_launched_ago": "Oct 1, 2024 (Preserved Baseline)", "launch_score": 92.4, "freshness_decay": 0.65, "integrity_multiplier": 1.05, "composite_velocity": 63.1, "cohort": "historical", "metrics": { "github_stars": 76000, "huggingface_downloads": 2800000, "benchmark_mats": "WER 7.1% | Speedup 8.1x", "throughput_tokens_sec": 340 }, "actions": { "try_url": "https://huggingface.co/openai/whisper-large-v3-turbo", "github_url": "https://github.com/openai/whisper", "huggingface_url": "https://huggingface.co/openai/whisper-large-v3-turbo", "paper_or_announcement_url": "https://github.com/openai/whisper" } }, { "id": "rel-h-107", "rank": 107, "name": "Ollama 0.5.x Local Model Engine Baseline", "provider": "Ollama Project", "maker_handle": "@ollama", "slug": "ollama-0-5-historical", "tagline": "Historic local runtime release introducing native tool calling, streaming JSON schema validation, and multi-modal support.", "category": "Coding Agents & Dev Tools", "subview_id": "historical", "architecture": "llama.cpp Core with Custom Go HTTP Server", "parameter_count": "Local Runtime", "context_window": "128k tokens", "license": "MIT License", "timeframe": "monthly", "launched_at": "2024-12-10T12:00:00Z", "release_date": "Dec 10, 2024", "formatted_launched_ago": "Dec 10, 2024 (Preserved Baseline)", "launch_score": 89.2, "freshness_decay": 0.68, "integrity_multiplier": 1.06, "composite_velocity": 64.3, "cohort": "historical", "metrics": { "github_stars": 125000, "huggingface_downloads": 5200000, "benchmark_mats": "Local Memory Footprint <45MB", "throughput_tokens_sec": 120 }, "actions": { "try_url": "https://ollama.com", "github_url": "https://github.com/ollama/ollama", "paper_or_announcement_url": "https://ollama.com/blog" } }, { "id": "rel-h-108", "rank": 108, "name": "LeRobot Physical AI & Robotics Library", "provider": "Hugging Face Robotics", "maker_handle": "@huggingface", "slug": "lerobot-historical", "tagline": "Pioneering open-source physical AI library providing PyTorch models, datasets, and simulation environments for real-world robotics.", "category": "Consumer Agents & Platforms", "subview_id": "historical", "architecture": "Action Chunking Transformer (ACT) & Diffusion Policy", "parameter_count": "Robotics Framework", "context_window": "N/A", "license": "Apache 2.0", "timeframe": "monthly", "launched_at": "2024-05-15T09:00:00Z", "release_date": "May 15, 2024", "formatted_launched_ago": "May 15, 2024 (Preserved Baseline)", "launch_score": 89.9, "freshness_decay": 0.6, "integrity_multiplier": 1.05, "composite_velocity": 56.6, "cohort": "historical", "metrics": { "github_stars": 19800, "huggingface_downloads": 140000, "benchmark_mats": "Bimanual Manipulation 92.0% Success Rate", "throughput_tokens_sec": 60 }, "actions": { "try_url": "https://github.com/huggingface/lerobot", "github_url": "https://github.com/huggingface/lerobot", "huggingface_url": "https://huggingface.co/lerobot", "paper_or_announcement_url": "https://huggingface.co/blog/lerobot" } }, { "id": "rel-h-109", "rank": 109, "name": "Mistral Pixtral Large 124B (Initial Release)", "provider": "Mistral AI", "maker_handle": "@MistralAI", "slug": "mistral-pixtral-large-historical", "tagline": "Initial European open-weights multimodal flagship designed for high-resolution document parsing and multi-image context.", "category": "Multimodal, Video & Speech", "subview_id": "historical", "architecture": "Multimodal Decoder Transformer with 400M Vision Encoder", "parameter_count": "124B", "context_window": "128k tokens", "license": "Mistral Research License", "timeframe": "monthly", "launched_at": "2024-11-18T10:00:00Z", "release_date": "Nov 18, 2024", "formatted_launched_ago": "Nov 18, 2024 (Preserved Baseline)", "launch_score": 91.2, "freshness_decay": 0.67, "integrity_multiplier": 1.04, "composite_velocity": 63.5, "cohort": "historical", "metrics": { "github_stars": 28000, "huggingface_downloads": 640000, "benchmark_mats": "MMMU 69.4% | DocVQA 93.8%", "throughput_tokens_sec": 42 }, "actions": { "try_url": "https://chat.mistral.ai", "github_url": "https://github.com/mistralai", "huggingface_url": "https://huggingface.co/mistralai", "paper_or_announcement_url": "https://mistral.ai/news/pixtral-large" } }, { "id": "rel-h-110", "rank": 110, "name": "FLUX.1 Schnell & Dev 12B Initial Release", "provider": "Black Forest Labs", "maker_handle": "@bfl_ml", "slug": "flux-1-schnell-historical", "tagline": "Landmark open-weights 12B rectified flow transformer delivering photorealistic visual synthesis in 1-4 step inference.", "category": "Multimodal, Video & Speech", "subview_id": "historical", "architecture": "Multimodal Diffusion Transformer (MMDiT)", "parameter_count": "12B", "context_window": "512 text tokens", "license": "Apache 2.0 (Schnell) / Non-Commercial (Dev)", "timeframe": "monthly", "launched_at": "2024-08-01T09:00:00Z", "release_date": "Aug 1, 2024", "formatted_launched_ago": "Aug 1, 2024 (Preserved Baseline)", "launch_score": 93.9, "freshness_decay": 0.63, "integrity_multiplier": 1.05, "composite_velocity": 62.1, "cohort": "historical", "metrics": { "github_stars": 36000, "huggingface_downloads": 5100000, "benchmark_mats": "GenEval 0.81 | Prompt Alignment 94.2%", "throughput_tokens_sec": 32 }, "actions": { "try_url": "https://huggingface.co/black-forest-labs/FLUX.1-schnell", "github_url": "https://github.com/black-forest-labs/flux", "huggingface_url": "https://huggingface.co/black-forest-labs/FLUX.1-schnell", "paper_or_announcement_url": "https://blackforestlabs.ai" } }, { "id": "rel-h-111", "rank": 111, "name": "vLLM 0.6.x PagedAttention Core Baseline", "provider": "vLLM Project", "maker_handle": "@vllm_project", "slug": "vllm-0-6-historical", "tagline": "The foundational open-source high-throughput LLM serving engine establishing the PagedAttention memory paradigm.", "category": "Coding Agents & Dev Tools", "subview_id": "historical", "architecture": "PagedAttention GPU Kernel Architecture", "parameter_count": "Serving Framework", "context_window": "N/A", "license": "Apache 2.0", "timeframe": "monthly", "launched_at": "2024-11-12T11:00:00Z", "release_date": "Nov 12, 2024", "formatted_launched_ago": "Nov 12, 2024 (Preserved Baseline)", "launch_score": 93.1, "freshness_decay": 0.66, "integrity_multiplier": 1.08, "composite_velocity": 66.4, "cohort": "historical", "metrics": { "github_stars": 44000, "huggingface_downloads": 1800000, "benchmark_mats": "Throughput baseline establishing 2,000+ t/s serving", "throughput_tokens_sec": 2000 }, "actions": { "try_url": "https://github.com/vllm-project/vllm", "github_url": "https://github.com/vllm-project/vllm", "paper_or_announcement_url": "https://github.com/vllm-project/vllm/releases" } }, { "id": "rel-h-112", "rank": 112, "name": "Stagehand 0.8 Playwright Autonomous Bridge", "provider": "Browserbase", "maker_handle": "@browserbase", "slug": "stagehand-0-8-historical", "tagline": "Initial open-source release of the declarative act/observe/extract SDK for browser-based agents.", "category": "Consumer Agents & Platforms", "subview_id": "historical", "architecture": "Autonomous DOM Healing Agent Layer", "parameter_count": "Typescript SDK", "context_window": "N/A", "license": "MIT License", "timeframe": "monthly", "launched_at": "2024-11-20T18:00:00Z", "release_date": "Nov 20, 2024", "formatted_launched_ago": "Nov 20, 2024 (Preserved Baseline)", "launch_score": 91.8, "freshness_decay": 0.67, "integrity_multiplier": 1.06, "composite_velocity": 65.2, "cohort": "historical", "metrics": { "github_stars": 18000, "huggingface_downloads": 95000, "benchmark_mats": "DOM Action Resilience 96.8%", "throughput_tokens_sec": 50 }, "actions": { "try_url": "https://stagehand.dev", "github_url": "https://github.com/browserbase/stagehand", "paper_or_announcement_url": "https://stagehand.dev" } }, { "id": "rel-h-113", "rank": 113, "name": "Browser-Use 0.9 Open Source Web Agent", "provider": "Browser-Use Open Source", "maker_handle": "@browser_use", "slug": "browser-use-0-9-historical", "tagline": "Early viral release connecting multimodal vision models to Playwright for autonomous web navigation.", "category": "Consumer Agents & Platforms", "subview_id": "historical", "architecture": "Vision-DOM Grounding Controller & Playwright Driver", "parameter_count": "Framework Library", "context_window": "N/A", "license": "MIT License", "timeframe": "monthly", "launched_at": "2024-12-28T20:00:00Z", "release_date": "Dec 28, 2024", "formatted_launched_ago": "Dec 28, 2024 (Preserved Baseline)", "launch_score": 96.8, "freshness_decay": 0.69, "integrity_multiplier": 1.1, "composite_velocity": 73.5, "cohort": "historical", "metrics": { "github_stars": 41000, "huggingface_downloads": 280000, "benchmark_mats": "WebVoyager 82.4%", "throughput_tokens_sec": 45 }, "actions": { "try_url": "https://github.com/browser-use/browser-use", "github_url": "https://github.com/browser-use/browser-use", "paper_or_announcement_url": "https://github.com/browser-use/browser-use/releases" } }, { "id": "rel-h-114", "rank": 114, "name": "Qwen 2.5 Coder 32B (November 2024 Initial Release)", "provider": "Alibaba Qwen", "maker_handle": "@Alibaba_Qwen", "slug": "qwen-2-5-coder-historical", "tagline": "The open-weights coding model that first matched GPT-4o on HumanEval and MultiPL-E in late 2024.", "category": "Coding Agents & Dev Tools", "subview_id": "historical", "architecture": "Dense Decoder-Only Transformer (GQA, RoPE)", "parameter_count": "32.5B", "context_window": "128k tokens", "license": "Apache 2.0", "timeframe": "monthly", "launched_at": "2024-11-12T18:00:00Z", "release_date": "Nov 12, 2024", "formatted_launched_ago": "Nov 12, 2024 (Preserved Baseline)", "launch_score": 96.1, "freshness_decay": 0.66, "integrity_multiplier": 1.08, "composite_velocity": 68.5, "cohort": "historical", "metrics": { "github_stars": 36000, "huggingface_downloads": 1850000, "benchmark_mats": "HumanEval 92.7% | MultiPL-E 83.1%", "throughput_tokens_sec": 78 }, "actions": { "try_url": "https://huggingface.co/Qwen/Qwen2.5-Coder-32B-Instruct", "github_url": "https://github.com/QwenLM/Qwen2.5-Coder", "huggingface_url": "https://huggingface.co/Qwen/Qwen2.5-Coder-32B-Instruct", "paper_or_announcement_url": "https://qwenlm.github.io/blog" } }, { "id": "rel-h-115", "rank": 115, "name": "Gemini 2.0 Flash Experimental Baseline", "provider": "Google DeepMind", "maker_handle": "@GoogleDeepMind", "slug": "gemini-2-0-flash-exp-historical", "tagline": "Historical milestone introducing native real-time multimodal live streaming and low-latency audio input/output.", "category": "Frontier Models & Flagships", "subview_id": "historical", "architecture": "Multimodal Sparse Mixture of Experts", "parameter_count": "Undisclosed", "context_window": "1M tokens", "license": "Proprietary Commercial API", "timeframe": "monthly", "launched_at": "2024-12-11T16:00:00Z", "release_date": "Dec 11, 2024", "formatted_launched_ago": "Dec 11, 2024 (Preserved Baseline)", "launch_score": 94.8, "freshness_decay": 0.68, "integrity_multiplier": 1.06, "composite_velocity": 68.3, "cohort": "historical", "metrics": { "github_stars": 22000, "huggingface_downloads": 650000, "benchmark_mats": "MMMU 72.8% | Video-MME 83.1%", "throughput_tokens_sec": 145 }, "actions": { "try_url": "https://aistudio.google.com", "paper_or_announcement_url": "https://deepmind.google/technologies/gemini" } } ] } }