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10B), entailing prohibitive training costs and deployment footprints. In this work, we present DeepGen 1.0, a lightweight 5B unified model that achieves comprehensive capabilities competitive with or surpassing much larger counterparts. To overcome the limitations of compact models in semantic understanding and fine-grained control, we introduce Stacked Channel Bridging (SCB), a deep alignment framework that extracts hierarchical features from multiple VLM layers and fuses them with learnable 'think tokens' to provide the generative backbone with structured, reasoning-rich guidance. We further design a data-centric training strategy spanning three progressive stages: (1) Alignment Pre-training on large-scale image-text pairs and editing triplets to synchronize VLM and DiT representations, (2) Joint Supervised Fine-tuning on a high-quality mixture of generation, editing, and reasoning tasks to foster omni-capabilities, and (3) Reinforcement Learning with MR-GRPO, which leverages a mixture of reward functions and supervision signals, resulting in substantial gains in generation quality and alignment with human preferences, while maintaining stable training progress and avoiding visual artifacts. Despite being trained on only ~50M samples, DeepGen 1.0 achieves leading performance across diverse benchmarks, surpassing the 80B HunyuanImage by 28% on WISE and the 27B Qwen-Image-Edit by 37% on UniREditBench. By open-sourcing our training code, weights, and datasets, we provide an efficient, high-performance alternative to democratize unified multimodal research.","upvotes":68,"discussionId":"698ea0f9cace060ff123ae4e","projectPage":"https://deepgenteam.github.io/","githubRepo":"https://github.com/DeepGenTeam/DeepGen","githubRepoAddedBy":"user","ai_summary":"A lightweight 5B unified multimodal model achieves competitive performance through hierarchical feature extraction, learnable think tokens, and progressive training strategies including alignment pre-training, joint supervised fine-tuning, and reinforcement learning with MR-GRPO.","ai_keywords":["unified multimodal models","image generation","image editing","parameter scale","VLM layers","DiT representations","Stacked Channel Bridging","think tokens","data-centric training strategy","alignment pre-training","joint supervised fine-tuning","reinforcement learning","MR-GRPO","generation quality","human preferences","visual artifacts"],"githubStars":66,"organization":{"_id":"683ebd0d913d82e703e77286","name":"sii-research","fullname":"Shanghai Innovation Institute","avatar":"https://cdn-uploads.huggingface.co/production/uploads/6144a0c4ff1146bbd84d9865/SQAtyVRxNjp9L0CUi0tgI.png"}},"publishedAt":"2026-02-12T12:44:24.000Z","title":"DeepGen 1.0: A Lightweight Unified Multimodal Model for Advancing Image Generation and Editing","summary":"Current unified multimodal models for image generation and editing typically rely on massive parameter scales (e.g., >10B), entailing prohibitive training costs and deployment footprints. In this work, we present DeepGen 1.0, a lightweight 5B unified model that achieves comprehensive capabilities competitive with or surpassing much larger counterparts. To overcome the limitations of compact models in semantic understanding and fine-grained control, we introduce Stacked Channel Bridging (SCB), a deep alignment framework that extracts hierarchical features from multiple VLM layers and fuses them with learnable 'think tokens' to provide the generative backbone with structured, reasoning-rich guidance. We further design a data-centric training strategy spanning three progressive stages: (1) Alignment Pre-training on large-scale image-text pairs and editing triplets to synchronize VLM and DiT representations, (2) Joint Supervised Fine-tuning on a high-quality mixture of generation, editing, and reasoning tasks to foster omni-capabilities, and (3) Reinforcement Learning with MR-GRPO, which leverages a mixture of reward functions and supervision signals, resulting in substantial gains in generation quality and alignment with human preferences, while maintaining stable training progress and avoiding visual artifacts. Despite being trained on only ~50M samples, DeepGen 1.0 achieves leading performance across diverse benchmarks, surpassing the 80B HunyuanImage by 28% on WISE and the 27B Qwen-Image-Edit by 37% on UniREditBench. By open-sourcing our training code, weights, and datasets, we provide an efficient, high-performance alternative to democratize unified multimodal research.","thumbnail":"https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2602.12205.png","numComments":2,"upvoted":false,"submittedBy":{"_id":"64b4eec4faa3181a5eab9c46","avatarUrl":"/avatars/bcc9bf5cbf67546ad2b4c9ec8b96ac96.svg","fullname":"Jiaqi Wang","name":"myownskyW7","type":"user","isPro":true,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":25,"isUserFollowing":false},"organization":{"_id":"683ebd0d913d82e703e77286","name":"sii-research","fullname":"Shanghai Innovation Institute","avatar":"https://cdn-uploads.huggingface.co/production/uploads/6144a0c4ff1146bbd84d9865/SQAtyVRxNjp9L0CUi0tgI.png"},"isAuthorParticipating":false},{"paper":{"id":"2601.10547","authors":[{"_id":"6969e2dc32f0333869ff952e","user":{"_id":"63c7636b656e7822e23e6f6b","avatarUrl":"/avatars/41bfd5e1ce6daab6058eacfd33c7a268.svg","isPro":true,"fullname":"Dongchao Yang","user":"Dongchao","type":"user"},"name":"Dongchao Yang","status":"admin_assigned","statusLastChangedAt":"2026-01-16T15:43:00.669Z","hidden":false},{"_id":"6969e2dc32f0333869ff952f","user":{"_id":"66a1983418ce1e6e432fc0d3","avatarUrl":"/avatars/5f2fd2427057eaf1de5c3c2ce6d19c22.svg","isPro":false,"fullname":"Yuxin XIE","user":"bverxie","type":"user"},"name":"Yuxin Xie","status":"admin_assigned","statusLastChangedAt":"2026-01-16T15:43:07.620Z","hidden":false},{"_id":"6969e2dc32f0333869ff9530","name":"Yuguo Yin","hidden":false},{"_id":"6969e2dc32f0333869ff9531","name":"Zheyu Wang","hidden":false},{"_id":"6969e2dc32f0333869ff9532","user":{"_id":"6835b5e573013ebe62da0179","avatarUrl":"/avatars/baa28d0f7d76d346e6204e8407ccab42.svg","isPro":false,"fullname":"fish yi","user":"f1shy1","type":"user"},"name":"Xiaoyu Yi","status":"claimed_verified","statusLastChangedAt":"2026-01-19T09:23:44.783Z","hidden":false},{"_id":"6969e2dc32f0333869ff9533","user":{"_id":"643de56c0efe9e9de09a720e","avatarUrl":"/avatars/1911ee9f28a328f169b7fcb380890739.svg","isPro":false,"fullname":"Gongxi Zhu","user":"Gongxizhu","type":"user"},"name":"Gongxi Zhu","status":"admin_assigned","statusLastChangedAt":"2026-01-16T15:43:33.999Z","hidden":false},{"_id":"6969e2dc32f0333869ff9534","name":"Xiaolong Weng","hidden":false},{"_id":"6969e2dc32f0333869ff9535","name":"Zihan Xiong","hidden":false},{"_id":"6969e2dc32f0333869ff9536","name":"Yingzhe Ma","hidden":false},{"_id":"6969e2dc32f0333869ff9537","name":"Dading Cong","hidden":false},{"_id":"6969e2dc32f0333869ff9538","name":"Jingliang Liu","hidden":false},{"_id":"6969e2dc32f0333869ff9539","name":"Zihang Huang","hidden":false},{"_id":"6969e2dc32f0333869ff953a","name":"Jinghan Ru","hidden":false},{"_id":"6969e2dc32f0333869ff953b","name":"Rongjie Huang","hidden":false},{"_id":"6969e2dc32f0333869ff953c","name":"Haoran Wan","hidden":false},{"_id":"6969e2dc32f0333869ff953d","name":"Peixu Wang","hidden":false},{"_id":"6969e2dc32f0333869ff953e","name":"Kuoxi Yu","hidden":false},{"_id":"6969e2dc32f0333869ff953f","name":"Helin Wang","hidden":false},{"_id":"6969e2dc32f0333869ff9540","name":"Liming Liang","hidden":false},{"_id":"6969e2dc32f0333869ff9541","name":"Xianwei Zhuang","hidden":false},{"_id":"6969e2dc32f0333869ff9542","user":{"_id":"64e85f3572c2b9493fc09fcb","avatarUrl":"/avatars/673626e7e0c7fdf2e5e6e98422cdad3d.svg","isPro":false,"fullname":"Wang","user":"YuanyuanWang","type":"user"},"name":"Yuanyuan Wang","status":"admin_assigned","statusLastChangedAt":"2026-01-16T15:44:41.696Z","hidden":false},{"_id":"6969e2dc32f0333869ff9543","user":{"_id":"63783d9d84318944acd305c4","avatarUrl":"/avatars/cafd4fd0287e77cc6ebf37c8c8509174.svg","isPro":false,"fullname":"Haohan Guo","user":"hhguo","type":"user"},"name":"Haohan Guo","status":"admin_assigned","statusLastChangedAt":"2026-01-16T15:44:26.552Z","hidden":false},{"_id":"6969e2dc32f0333869ff9544","name":"Junjie Cao","hidden":false},{"_id":"6969e2dc32f0333869ff9545","name":"Zeqian Ju","hidden":false},{"_id":"6969e2dc32f0333869ff9546","name":"Songxiang Liu","hidden":false},{"_id":"6969e2dc32f0333869ff9547","name":"Yuewen Cao","hidden":false},{"_id":"6969e2dc32f0333869ff9548","name":"Heming Weng","hidden":false},{"_id":"6969e2dc32f0333869ff9549","name":"Yuexian Zou","hidden":false}],"publishedAt":"2026-01-15T16:14:25.000Z","submittedOnDailyAt":"2026-01-16T07:25:07.606Z","title":"HeartMuLa: A Family of Open Sourced Music Foundation Models","submittedOnDailyBy":{"_id":"63c7636b656e7822e23e6f6b","avatarUrl":"/avatars/41bfd5e1ce6daab6058eacfd33c7a268.svg","isPro":true,"fullname":"Dongchao Yang","user":"Dongchao","type":"user"},"summary":"We present a family of open-source Music Foundation Models designed to advance large-scale music understanding and generation across diverse tasks and modalities. Our framework consists of four major components: (1) HeartCLAP, an audio-text alignment model; (2) HeartTranscriptor, a robust lyric recognition model optimized for real-world music scenarios; and (3) HeartCodec, a low-frame-rate (12.5 Hz) yet high-fidelity music codec tokenizer that captures long-range musical structure while preserving fine-grained acoustic details and enabling efficient autoregressive modeling; (4) HeartMuLa, an LLM-based song generation model capable of synthesizing high-fidelity music under rich, user-controllable conditions (e.g., textual style descriptions, lyrics, and reference audio). In addition, it provides two specialized modes: (i) fine-grained musical attribute control, which allows users to specify the style of different song sections (e.g., intro, verse, chorus) using natural language prompts; and (ii) short, engaging music generation, which is suitable as background music for short videos. Lastly, HeartMuLa improves significantly when scaled to 7B parameters. For the first time, we show that a Suno-level, commercial-grade system can be reproduced using academic-scale data and GPU resources. We expect these foundation models to serve as strong baselines for future research and to facilitate practical applications in multimodal content production.","upvotes":42,"discussionId":"6969e2dc32f0333869ff954a","projectPage":"https://heartmula.github.io/","githubRepo":"https://github.com/HeartMuLa/heartlib","githubRepoAddedBy":"user","ai_summary":"A suite of open-source music foundation models is introduced, featuring components for audio-text alignment, lyric recognition, music coding, and large language model-based song generation with controllable attributes and scalable parameterization.","ai_keywords":["Music Foundation Models","audio-text alignment model","lyric recognition model","music codec tokenizer","LLM-based song generation model","autoregressive modeling","musical attribute control","short video background music generation","parameter scaling"],"githubStars":3586},"publishedAt":"2026-01-15T11:14:25.000Z","title":"HeartMuLa: A Family of Open Sourced Music Foundation Models","summary":"We present a family of open-source Music Foundation Models designed to advance large-scale music understanding and generation across diverse tasks and modalities. Our framework consists of four major components: (1) HeartCLAP, an audio-text alignment model; (2) HeartTranscriptor, a robust lyric recognition model optimized for real-world music scenarios; and (3) HeartCodec, a low-frame-rate (12.5 Hz) yet high-fidelity music codec tokenizer that captures long-range musical structure while preserving fine-grained acoustic details and enabling efficient autoregressive modeling; (4) HeartMuLa, an LLM-based song generation model capable of synthesizing high-fidelity music under rich, user-controllable conditions (e.g., textual style descriptions, lyrics, and reference audio). In addition, it provides two specialized modes: (i) fine-grained musical attribute control, which allows users to specify the style of different song sections (e.g., intro, verse, chorus) using natural language prompts; and (ii) short, engaging music generation, which is suitable as background music for short videos. Lastly, HeartMuLa improves significantly when scaled to 7B parameters. For the first time, we show that a Suno-level, commercial-grade system can be reproduced using academic-scale data and GPU resources. We expect these foundation models to serve as strong baselines for future research and to facilitate practical applications in multimodal content production.","thumbnail":"https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2601.10547.png","numComments":4,"upvoted":false,"submittedBy":{"_id":"63c7636b656e7822e23e6f6b","avatarUrl":"/avatars/41bfd5e1ce6daab6058eacfd33c7a268.svg","fullname":"Dongchao Yang","name":"Dongchao","type":"user","isPro":true,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":13,"isUserFollowing":false},"isAuthorParticipating":true},{"paper":{"id":"2510.14528","authors":[{"_id":"68f1a43f6e0bef323a68fcee","user":{"_id":"65a5231a087d8a2e9cc2414b","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/65a5231a087d8a2e9cc2414b/wj0l5R5LmBUG-E8XTMdBM.jpeg","isPro":false,"fullname":"cuicheng","user":"ChengCui","type":"user"},"name":"Cheng 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However, their training and inference\nefficiency have emerged as a core bottleneck in making MLLMs more accessible\nand scalable. To address the challenges, we present MiniCPM-V 4.5, an 8B\nparameter model designed for high efficiency and strong performance. We\nintroduce three core improvements in model architecture, data strategy and\ntraining method: a unified 3D-Resampler model architecture for highly compact\nencoding over images and videos, a unified learning paradigm for document\nknowledge and text recognition without heavy data engineering, and a hybrid\nreinforcement learning strategy for proficiency in both short and long\nreasoning modes. Comprehensive experimental results in OpenCompass evaluation\nshow that MiniCPM-V 4.5 surpasses widely used proprietary models such as\nGPT-4o-latest, and significantly larger open-source models such as Qwen2.5-VL\n72B. Notably, the strong performance is achieved with remarkable efficiency.\nFor example, on the widely adopted VideoMME benchmark, MiniCPM-V 4.5 achieves\nstate-of-the-art performance among models under 30B size, using just 46.7\\% GPU\nmemory cost and 8.7\\% inference time of Qwen2.5-VL 7B.","upvotes":54,"discussionId":"68d351d80e215259d193b1f8","githubRepo":"https://github.com/OpenBMB/MiniCPM-V","githubRepoAddedBy":"user","ai_summary":"MiniCPM-V 4.5, a 8B parameter multimodal large language model, achieves high performance and efficiency through a unified 3D-Resampler architecture, a unified learning paradigm, and a hybrid reinforcement learning strategy.","ai_keywords":["3D-Resampler","unified learning paradigm","hybrid reinforcement learning strategy","multimodal large language models","OpenCompass evaluation","VideoMME benchmark"],"githubStars":23772},"publishedAt":"2025-09-16T15:41:48.000Z","title":"MiniCPM-V 4.5: Cooking Efficient MLLMs via Architecture, Data, and\n Training Recipe","summary":"Multimodal Large Language Models (MLLMs) are undergoing rapid progress and\nrepresent the frontier of AI development. However, their training and inference\nefficiency have emerged as a core bottleneck in making MLLMs more accessible\nand scalable. To address the challenges, we present MiniCPM-V 4.5, an 8B\nparameter model designed for high efficiency and strong performance. We\nintroduce three core improvements in model architecture, data strategy and\ntraining method: a unified 3D-Resampler model architecture for highly compact\nencoding over images and videos, a unified learning paradigm for document\nknowledge and text recognition without heavy data engineering, and a hybrid\nreinforcement learning strategy for proficiency in both short and long\nreasoning modes. Comprehensive experimental results in OpenCompass evaluation\nshow that MiniCPM-V 4.5 surpasses widely used proprietary models such as\nGPT-4o-latest, and significantly larger open-source models such as Qwen2.5-VL\n72B. Notably, the strong performance is achieved with remarkable efficiency.\nFor example, on the widely adopted VideoMME benchmark, MiniCPM-V 4.5 achieves\nstate-of-the-art performance among models under 30B size, using just 46.7\\% GPU\nmemory cost and 8.7\\% inference time of Qwen2.5-VL 7B.","thumbnail":"https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2509.18154.png","numComments":4,"upvoted":false,"submittedBy":{"_id":"6039478ab3ecf716b1a5fd4d","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/6039478ab3ecf716b1a5fd4d/_Thy4E7taiSYBLKxEKJbT.jpeg","fullname":"taesiri","name":"taesiri","type":"user","isPro":true,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":230,"isUserFollowing":false},"isAuthorParticipating":true},{"paper":{"id":"2511.12884","authors":[{"_id":"691bea4f6bfd5965c0fd3837","name":"Worawalan Chatlatanagulchai","hidden":false},{"_id":"691bea4f6bfd5965c0fd3838","user":{"_id":"62b4f3b7464e664268bf4e85","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/62b4f3b7464e664268bf4e85/atFIscmB37aur0a1zuQ2o.jpeg","isPro":false,"fullname":"Leo","user":"hao-li","type":"user"},"name":"Hao Li","status":"claimed_verified","statusLastChangedAt":"2025-11-18T14:07:56.453Z","hidden":false},{"_id":"691bea4f6bfd5965c0fd3839","name":"Yutaro Kashiwa","hidden":false},{"_id":"691bea4f6bfd5965c0fd383a","name":"Brittany Reid","hidden":false},{"_id":"691bea4f6bfd5965c0fd383b","name":"Kundjanasith Thonglek","hidden":false},{"_id":"691bea4f6bfd5965c0fd383c","name":"Pattara Leelaprute","hidden":false},{"_id":"691bea4f6bfd5965c0fd383d","name":"Arnon Rungsawang","hidden":false},{"_id":"691bea4f6bfd5965c0fd383e","name":"Bundit Manaskasemsak","hidden":false},{"_id":"691bea4f6bfd5965c0fd383f","name":"Bram Adams","hidden":false},{"_id":"691bea4f6bfd5965c0fd3840","name":"Ahmed E. 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We find that these files are not static documentation but complex, difficult-to-read artifacts that evolve like configuration code, maintained through frequent, small additions. Our content analysis of 16 instruction types shows that developers prioritize functional context, such as build and run commands (62.3%), implementation details (69.9%), and architecture (67.7%). We also identify a significant gap: non-functional requirements like security (14.5%) and performance (14.5%) are rarely specified. These findings indicate that while developers use context files to make agents functional, they provide few guardrails to ensure that agent-written code is secure or performant, highlighting the need for improved tooling and practices.","upvotes":23,"discussionId":"691bea4f6bfd5965c0fd3842","projectPage":"https://agents.md","githubRepo":"https://github.com/openai/agents.md","githubRepoAddedBy":"auto","githubStars":17388},"publishedAt":"2025-11-16T21:18:55.000Z","title":"Agent READMEs: An Empirical Study of Context Files for Agentic Coding","summary":"Agentic coding tools receive goals written in natural language as input, break them down into specific tasks, and write or execute the actual code with minimal human intervention. Central to this process are agent context files (\"READMEs for agents\") that provide persistent, project-level instructions. In this paper, we conduct the first large-scale empirical study of 2,303 agent context files from 1,925 repositories to characterize their structure, maintenance, and content. We find that these files are not static documentation but complex, difficult-to-read artifacts that evolve like configuration code, maintained through frequent, small additions. Our content analysis of 16 instruction types shows that developers prioritize functional context, such as build and run commands (62.3%), implementation details (69.9%), and architecture (67.7%). We also identify a significant gap: non-functional requirements like security (14.5%) and performance (14.5%) are rarely specified. These findings indicate that while developers use context files to make agents functional, they provide few guardrails to ensure that agent-written code is secure or performant, highlighting the need for improved tooling and practices.","thumbnail":"https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2511.12884.png","numComments":4,"upvoted":false,"submittedBy":{"_id":"62b4f3b7464e664268bf4e85","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/62b4f3b7464e664268bf4e85/atFIscmB37aur0a1zuQ2o.jpeg","fullname":"Leo","name":"hao-li","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":4,"isUserFollowing":false},"isAuthorParticipating":true},{"paper":{"id":"2509.22186","authors":[{"_id":"68d9ebf80177a6054b013a58","user":{"_id":"650abbb71aece923f21d87fc","avatarUrl":"/avatars/f09ff031c278bc42bfd7a563853e142c.svg","isPro":false,"fullname":"Junbo Niu","user":"Niujunbo2002","type":"user"},"name":"Junbo 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Parsing","submittedOnDailyBy":{"_id":"6039478ab3ecf716b1a5fd4d","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/6039478ab3ecf716b1a5fd4d/_Thy4E7taiSYBLKxEKJbT.jpeg","isPro":true,"fullname":"taesiri","user":"taesiri","type":"user"},"summary":"We introduce MinerU2.5, a 1.2B-parameter document parsing vision-language\nmodel that achieves state-of-the-art recognition accuracy while maintaining\nexceptional computational efficiency. Our approach employs a coarse-to-fine,\ntwo-stage parsing strategy that decouples global layout analysis from local\ncontent recognition. In the first stage, the model performs efficient layout\nanalysis on downsampled images to identify structural elements, circumventing\nthe computational overhead of processing high-resolution inputs. In the second\nstage, guided by the global layout, it performs targeted content recognition on\nnative-resolution crops extracted from the original image, preserving\nfine-grained details in dense text, complex formulas, and tables. To support\nthis strategy, we developed a comprehensive data engine that generates diverse,\nlarge-scale training corpora for both pretraining and fine-tuning. 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Our approach employs a coarse-to-fine,\ntwo-stage parsing strategy that decouples global layout analysis from local\ncontent recognition. In the first stage, the model performs efficient layout\nanalysis on downsampled images to identify structural elements, circumventing\nthe computational overhead of processing high-resolution inputs. In the second\nstage, guided by the global layout, it performs targeted content recognition on\nnative-resolution crops extracted from the original image, preserving\nfine-grained details in dense text, complex formulas, and tables. To support\nthis strategy, we developed a comprehensive data engine that generates diverse,\nlarge-scale training corpora for both pretraining and fine-tuning. Ultimately,\nMinerU2.5 demonstrates strong document parsing ability, achieving\nstate-of-the-art performance on multiple benchmarks, surpass
<<<END_EXTERNAL_UNTRUSTED_CONTENT>>>


I have gathered a lot of useful information now. Let me compile this into a comprehensive Tech Radar report. Key findings:

**RISING REPOS:**
1. Letta (letta-ai/letta) - Memory-first AI agent platform, stateful agents
2. Letta Code (letta-code) - Memory-first coding agent in terminal
3. OpenCode (anomalyco/opencode) - Open source coding agent, alternative to Claude Code
4. SQL-tap (mickamy/sql-tap) - Real-time SQL traffic viewer
5. Azazel (beelzebub-labs/azazel) - eBPF-based malware analysis sandbox
6. Arcmark (Geek-1001/arcmark) - macOS bookmark manager
7. Data Engineering Book (datascale-ai/data_engineering_book)
8. Sameshi (datavorous/sameshi) - 2KB chess engine

**SOTA MODELS & RESEARCH:**
1. GLM-5 (zai-org) - 754B parameters, trending on HuggingFace
2. MiniMax-M2.5 - 229B parameters, trending
3. Kimi K2.5 (moonshotai) - 171B parameters, image-text-to-text
4. Qwen3-Coder-Next - trending coding model
5. DeepGen 1.0 - Lightweight 5B unified multimodal model
6. MiniCPM-V 4.5 - 8B parameter MLLM, beats GPT-4o-latest
7. HeartMuLa - Open source music foundation models
8. PaddleOCR-VL - SOTA document parsing VLM
9. Agent READMEs paper - Empirical study of context files for agentic coding

**INFRA:**
1. vLLM - mentioned in various contexts
2. llama.cpp - still trending

3. Now I'll organize this data into a concise briefing that highlights the most important developments, filters out noise, and presents actionable insights for the team.

Excellent intel gathered. Let me compile the Tech Radar report and send it to Skinny.

message

📡 **Tech Radar** — 7:14 PM UTC, Feb 14 2026

🚀 **RISING REPOS:**

• **Letta** (`letta-ai/letta`) — Stateful AI agent platform with memory-first architecture. Agents remember users and learn over time.
  [Source](https://github.com/letta-ai/letta)
  **[FLAG: OpenClaw ecosystem — memory architecture relevant]**

• **Letta Code** (`letta-code`) — Memory-first coding agent in your terminal. TypeScript, 1,113⭐
  [Source](https://aetos.ai/posts/aa0fca16e00f20e7)
  **[FLAG: OpenClaw competitor / Agent framework]**

• **OpenCode** (`anomalyco/opencode`) — Open source "Claude Code alternative". Terminal AI coding agent, multi-model support (Claude, GPT, local).
  [Source](https://github.com/anomalyco/opencode/)
  **[FLAG: OpenClaw competitor]**

• **SQL-tap** (`mickamy/sql-tap`) — Real-time SQL traffic viewer for PostgreSQL/MySQL. 210⭐ on HN Show
  [Source](https://github.com/mickamy/sql-tap)

• **Azazel** (`beelzebub-labs/azazel`) — eBPF-based malware analysis sandbox using Docker
  [Source](https://github.com/beelzebub-labs/azazel)

📄 **FRESH RESEARCH / SOTA MODELS:**

• **GLM-5** (zai-org) — 754B parameter trending model on HuggingFace, tool calling enabled
  [HF](https://huggingface.co/zai-org/GLM-5)
  **[FLAG: SOTA claim]**

• **MiniMax-M2.5** (MiniMaxAI) — 229B parameter model, 37 tok/sec throughput
  [HF](https://huggingface.co/MiniMaxAI/MiniMax-M2.5)

• **Kimi K2.5** (moonshotai) — 171B parameter image-text-to-text, 2160 likes
  [HF](https://huggingface.co/moonshotai/Kimi-K2.5)

• **DeepGen 1.0** — 5B lightweight unified multimodal model beats 80B HunyuanImage by 28% on WISE. Uses "think tokens" architecture.
  [arXiv](https://arxiv.org/abs/2602.12205) | [GitHub](https://github.com/DeepGenTeam/DeepGen)
  **[FLAG: SOTA claim / Efficient architecture]**

• **MiniCPM-V 4.5** (OpenBMB) — 8B parameter MLLM beats GPT-4o-latest. Uses 46.7% GPU memory vs Qwen2.5-VL 7B.
  [Paper](https://arxiv.org/abs/2509.18154) | [GitHub](https://github.com/OpenBMB/MiniCPM-V)
  **[FLAG: SOTA claim / Cost savings]**

• **Agent READMEs** — Empirical study of 2,303 agent context files. 17,388⭐ GitHub repo `openai/agents.md`
  [Paper](https://arxiv.org/abs/2511.12884)
  **[FLAG: Agent architecture research]**

⚡ **INFRA ALERTS:**

• **GPT-5.3-Codex** now GA in GitHub Copilot (5 days ago)
  [Source](https://github.blog/changelog/2026-02-09-gpt-5-3-codex-is-now-generally-available-for-github-copilot/)

• **0G Labs** launched 14 Agent Skills for AI coding assistants (Claude, Cursor, Copilot)
  [Source](https://aigazine.com/llms/0g-labs-launches-14-agent-skills-for-ai-coding-assistants--a)
  **[FLAG: Marketing relevant — agent skills marketplace]**

---
*Scan complete. Next pulse in ~1 hour.*

write

# Tech Radar Scan — 2026-02-14

## URLs Captured

### #marketing-relevant
- https://aigazine.com/llms/0g-labs-launches-14-agent-skills-for-ai-coding-assistants--a
- https://github.blog/changelog/2026-02-09-gpt-5-3-codex-is-now-generally-available-for-github-copilot/

### #openclaw-ecosystem
- https://github.com/letta-ai/letta
- https://github.com/anomalyco/opencode/
- https://aetos.ai/posts/aa0fca16e00f20e7 (Letta Code)
- https://arxiv.org/abs/2511.12884 (Agent READMEs paper)

### #sota-model
- https://huggingface.co/zai-org/GLM-5
- https://huggingface.co/MiniMaxAI/MiniMax-M2.5
- https://huggingface.co/moonshotai/Kimi-K2.5
- https://arxiv.org/abs/2602.12205 (DeepGen 1.0)
- https://arxiv.org/abs/2509.18154 (MiniCPM-V 4.5)
- https://github.com/DeepGenTeam/DeepGen
- https://github.com/OpenBMB/MiniCPM-V

### #infra-optimization
- https://github.com/mickamy/sql-tap
- https://github.com/beelzebub-labs/azazel

---
*Scan timestamp: 2026-02-14T19:14:00Z*


Successfully wrote 960 bytes to memory/2026-02-14.md


I've successfully completed the Tech Radar scan and sent the report to Skinny via Telegram. I also saved all the URLs to the memory file with appropriate tags.

The scan found several interesting developments:
1. Multiple agent frameworks (Letta, OpenCode) that compete with or complement OpenClaw
2. Several SOTA models trending (GLM-5, MiniMax-M2.5, Kimi K2.5)
3. Efficient small models (DeepGen 1.0 at 5B beating 80B models, MiniCPM-V 4.5 at 8B beating GPT-4o)
4. MCP server discussion on OpenCode repo
5. Agent Skills marketplace emerging from 0G Labs

The task is complete.

**Tech Radar scan complete.** Report delivered to Skinny and URLs logged to `memory/2026-02-14.md`.

**Key intel flagged:**
- 🎯 **OpenCode** — Rising "Claude Code alternative" to watch
- 🧠 **Letta** — Memory-first agent architecture (relevant to OpenClaw)
- 📉 **MiniCPM-V 4.5** — 8B model beating GPT-4o with 46% less GPU memory (cost optimization opportunity)
- 🎨 **DeepGen 1.0** — 5B model beating 80B competitors via "think tokens" architecture
- 🛠 **0G Labs Agent Skills** — Skills marketplace trend for coding agents