Si vous voulez un accessoire utile pour l'IA, c'est ça qu'il vous faut. #IA #PrimeDay2025
TOOLS
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Sort Models by Creation Date in Replicate HTTP API
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You can now sort models by creation date in our HTTP API. This makes it easier to find the hottest new models programmatically. https://
replicate.com/changelog/2025
-10-08-models-api-sorting
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AI21 Labs Launches New 3B Reasoning Open Model Hybrid Architecture
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Check out our new 3B reasonong open model – hybrid mamba nd transformer. It's ranked above Granite, Gemma, llma, etc. on the tiny category.
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Ant Ling releases 1T-params open-source coding model
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Ant Ling introduced a new 1T-params, non thinking open source model with a good performance on coding tasks. 1T
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Ollama’s bloated wrapper fails to match ggml’s efficiency
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do not use Ollama ggerganov wrote blazing-fast
C++ inference (ggml, llama.cpp) then Ollama wrapped it
in a bloated binary and is now somehow the face of local LLMs
soaking up VC hype and it's not even a good wrapper lol -
First 1:30+ video created with Sora Extend
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This is the first >1:30 video I’ve seen someone make with Sora Extend. Very cool! https://
x.com/O_on_X/status/
/O_on_X/status/1975770528247128292
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Run Claude Code Locally on Your Own GPU Setup
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tired of Anthropic’s weekly limits and nerfed models? with one command and a few GPUs,
you can route Claude Code to your own local LLM Buy a GPU p.s. full video tutorial pinned at the top of my profile -
Midjourney Releases V7: First New AI Image Model in Year
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Midjourney releases V7, its first new AI image model in nearly a year #AI #AIio #AIInnovation #ML #DataScience #Futureofwork @timnitgebru @oriolvinyalsml @ceobillionaire @soumithchintala @waitin4agi_ @sallyeaves @bernardmarr
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Jamba 1.5 Large Now Available for Local Inference Download
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5/5 Available today for download & local inference on @huggingface
, @kaggle
, @lmstudio
, and llama.cpp. -
Jamba Reasoning 3B: Exceptional Performance on Extended Contexts
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3/5 Where most other tiny models choke at context lengths above 8K, Jamba Reasoning 3B stays steady, with a consistent 30-40 tokens/second on an M3 MacBook Pro, regardless of context size. This is up to an order of magnitude faster than other on-device models.