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π What happened in AI in 2025? π
We prepared the 2025 version of the HF AI Timeline Grid, highlighting open vs API-based model releases, and allowing you to browse and filter by access, modality, and release type!
Play with it here:
2025-ai-timeline/2025-ai-timeline
Here's my personal quarterly TL;DR:
1οΈβ£ Q1 β Learning to Reason
Deepseek not only releases a top-notch reasoning model, but shows how to train them and compete with closed frontier models. OpenAI debuts Deep Research.
Significant milestones: DeepSeek R1 & R1-Zero, Qwen 2.5 VL, OpenAI Deep Research, Gemini 2.5 Pro (experimental)
2οΈβ£ Q2 β Multimodality and Coding
More LLMs embrace multimodality by default, and there's a surge in coding agents. Strong vision, audio, and generative models emerge.
Significant milestones: Llama 4, Qwen 3, Imagen 4, OpenAI Codex, Google Jules, Claude 4
3οΈβ£ Q3 β "Gold" rush, OpenAI opens up, the community goes bananas
Flagship models get gold in Math olympiads and hard benchmarks. OpenAI releases strong open source models and Google releases the much anticipated nano-banana for image generation and editing. Agentic workflows become commonplace.
Significant milestones: Gemini and OpenAI IMO Gold, gpt-oss, Gemini 2.5 Flash Image, Grok 4, Claude Sonnet 4.5
4οΈβ£ Q4 β Mistral returns, leaderboard hill-climbing
Mistral is back with updated model families. All labs release impressive models to wrap up the year!
Significant milestones: Claude Opus 4.5, DeepSeek Math V2, FLUX 2, GPT 5.1, Kimi K2 Thinking, Nano Banana Pro, GLM 4.7, Gemini 3, Mistral 3, MiniMax M2.1 π€―
Credits
π NHLOCAL for the source data https://github.com/NHLOCAL/AiTimeline
π«‘ @reach-vb for the original idea, design and recipe
π @ariG23498 and yours truly for compiling and verifying the 2025 edition
π₯³ Here's to 2026, wishing it becomes the best year ever for open releases and on-device-first use-cases! π₯
We prepared the 2025 version of the HF AI Timeline Grid, highlighting open vs API-based model releases, and allowing you to browse and filter by access, modality, and release type!
Play with it here:
2025-ai-timeline/2025-ai-timeline
Here's my personal quarterly TL;DR:
1οΈβ£ Q1 β Learning to Reason
Deepseek not only releases a top-notch reasoning model, but shows how to train them and compete with closed frontier models. OpenAI debuts Deep Research.
Significant milestones: DeepSeek R1 & R1-Zero, Qwen 2.5 VL, OpenAI Deep Research, Gemini 2.5 Pro (experimental)
2οΈβ£ Q2 β Multimodality and Coding
More LLMs embrace multimodality by default, and there's a surge in coding agents. Strong vision, audio, and generative models emerge.
Significant milestones: Llama 4, Qwen 3, Imagen 4, OpenAI Codex, Google Jules, Claude 4
3οΈβ£ Q3 β "Gold" rush, OpenAI opens up, the community goes bananas
Flagship models get gold in Math olympiads and hard benchmarks. OpenAI releases strong open source models and Google releases the much anticipated nano-banana for image generation and editing. Agentic workflows become commonplace.
Significant milestones: Gemini and OpenAI IMO Gold, gpt-oss, Gemini 2.5 Flash Image, Grok 4, Claude Sonnet 4.5
4οΈβ£ Q4 β Mistral returns, leaderboard hill-climbing
Mistral is back with updated model families. All labs release impressive models to wrap up the year!
Significant milestones: Claude Opus 4.5, DeepSeek Math V2, FLUX 2, GPT 5.1, Kimi K2 Thinking, Nano Banana Pro, GLM 4.7, Gemini 3, Mistral 3, MiniMax M2.1 π€―
Credits
π NHLOCAL for the source data https://github.com/NHLOCAL/AiTimeline
π«‘ @reach-vb for the original idea, design and recipe
π @ariG23498 and yours truly for compiling and verifying the 2025 edition
π₯³ Here's to 2026, wishing it becomes the best year ever for open releases and on-device-first use-cases! π₯
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great job!
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Finally, the ground truth / AlexNetβs original source code is available to all.
Context: AlexNet had a historic win in the 2012 ImageNet Large Scale Visual Recognition Challenge (ILSVRC), reducing error rate from 26% (previous best) to 15.3%. Itβs a deep CNN with 8 layers (5 convolutional + 3 fully connected), pioneering the use of ReLU activations for faster training, dropout for regularization, and GPU acceleration for large-scale learning. This moment marked the beginning of the deep learning revolution, inspiring architectures like VGG, ResNet, and modern transformers.
Code: https://github.com/computerhistory/AlexNet-Source-Code
Context: AlexNet had a historic win in the 2012 ImageNet Large Scale Visual Recognition Challenge (ILSVRC), reducing error rate from 26% (previous best) to 15.3%. Itβs a deep CNN with 8 layers (5 convolutional + 3 fully connected), pioneering the use of ReLU activations for faster training, dropout for regularization, and GPU acceleration for large-scale learning. This moment marked the beginning of the deep learning revolution, inspiring architectures like VGG, ResNet, and modern transformers.
Code: https://github.com/computerhistory/AlexNet-Source-Code
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RWKV7-G1 0.1B π₯ Pure RNN reasoning model released by RWKV
Model: BlinkDL/rwkv7-g1
paper: RWKV-7 "Goose" with Expressive Dynamic State Evolution (2503.14456)
β¨ Apache2.0
β¨ Supports 100+ languages
β¨ 0.1 B runs smoothly on low power devices
β¨ 0.4B/1.5B/2.9B are coming soon!!
Model: BlinkDL/rwkv7-g1
paper: RWKV-7 "Goose" with Expressive Dynamic State Evolution (2503.14456)
β¨ Apache2.0
β¨ Supports 100+ languages
β¨ 0.1 B runs smoothly on low power devices
β¨ 0.4B/1.5B/2.9B are coming soon!!
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