Jorge GX

Cloud Solution Engineer for Data Applications & Belgian K8s Meetup coorganisor

[Carlos E. Perez @IntuitMachine]

https://t.co/CHEk211f7Y

[Chubby♨️ @kimmonismus]

AI buying agents are coming - faster than anticipated. To be fair, I hate spending so much time looking for better prices for products.

They are coming this year, in a few months, says VISA CEO.

[Microsoft Azure @Azure]

The o3-mini model is now in Microsoft Azure OpenAI Service! o3-mini’s advanced capabilities and efficiency gains make it a powerful tool for developers and enterprises looking to optimize their AI apps. Learn more: https://t.co/ayKPg9r1I8

[Microsoft AI Frontiers @ms_aifrontiers]

HUGE: The biggest upgrade to #AutoGen just dropped!

v0.4 (stable) is finally here.

For detail, checkout the blog below.

[Microsoft Azure @Azure]

Mistral-Large-2411 is now available in the Azure AI Model Catalog! This cutting-edge AI model offers:

🔹 Enhanced system prompts
🔹 128K token context length for complex inputs
🔹 Advanced reasoning and function calling capabilities

Read the blog: https://t.co/mvY7CY6eSH

[clem 🤗 @ClementDelangue]

Just 10 days after o1's public debut, we’re thrilled to unveil the open-source version of the groundbreaking technique behind its success: scaling test-time compute 🧠💡

By giving models more "time to think," LLaMA 1B outperforms LLaMA 8B in math—beating a model 8x its size. The full recipe is open-source🤯

This is the power of open science and open-source AI! 🌍✨

[Microsoft AI Frontiers @ms_aifrontiers]

📢Introducing Magentic-One, a generalist 5-agent multi-agent system for solving open-ended web- and file-based tasks. 🤖🤖🤖🤖🤖

Magentic-One represents a significant step towards agents that can complete tasks that people encounter in their daily lives and can achieve strong performance and generalization across THREE challenging agentic benchmarks: GAIA, WebArena, and Assistant.

We are releasing an open-source implementation in #AutoGen, our popular open-source framework for developing multi-agent applications. Checkout the technical report, blog, and implementation below 👇

@MSFTResearch @Microsoft
#AutoGen #Agents #opensource

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[Jorge GX @JorgeGarciaX]

@kimmonismus https://t.co/eK4DiGMyFj

[Ethan Mollick @emollick]

New longitudinal GenAI adoption survey by my colleagues at Wharton (not me), surveying 800 senior managers at big firms, finds usage doubled in a year: 72% use at least once a week.

The vast majority report positive impacts. The pace of real world AI adoption continues rapidly.

[Jorge GX @JorgeGarciaX]

@jerryjliu0 Will you open source the solution? Would be very interested to see your approach in respect to images and charts!:-)

[AK @_akhaliq]

Daily Research Bot

an AI-powered Discord bot that helps you stay on top of new AI-related research, with a focus on low-resource language translation tasks. It summarizes recent papers from sources like Hugging Face and Elvis Saravia's blog, providing concise insights and potential applications.

[Greg Kamradt @GregKamradt]

Dev day thread

Starting with real time API

[AI at Meta @AIatMeta]

📣 Introducing Llama 3.2: Lightweight models for edge devices, vision models and more!

What’s new?
• Llama 3.2 1B & 3B models deliver state-of-the-art capabilities for their class for several on-device use cases — with support for @Arm, @MediaTek & @Qualcomm on day one.
• Llama 3.2 11B & 90B vision models deliver performance competitive with leading closed models — and can be used as drop-in replacements for Llama 3.1 8B & 70B.
• New Llama Guard models to support multimodal use cases and edge deployments.
• The first official distro of Llama Stack simplifies and supercharges the way developers & enterprises can build around Llama to support agentic applications and more.

Details in the full announcement ➡️ https://t.co/1bnEeLY9qf
Download Llama 3.2 models ➡️ https://t.co/DZoTQvESbG

These models are available to download now directly from Meta and @HuggingFace — and will be available across offerings from 25+ partners that are rolling out starting today, including @accenture, @awscloud, @AMD, @azure, @Databricks, @Dell, @Deloitte, @FireworksAI_HQ, @GoogleCloud, @GroqInc, @IBMwatsonx, @Infosys, @Intel, @kaggle, @NVIDIA, @OracleCloud, @PwC, @scale_AI, @snowflakeDB, @togethercompute and more.

With Llama 3.2 we’re making it possible to run Llama in even more places, with even more flexible capabilities. We’ve said it before and we’ll say it again: open source AI is how we ensure that these innovations reflect the global community they’re built for and benefit everyone. We’re continuing our drive to make open source the standard with Llama 3.2.

[Rohan Paul @rohanpaul_ai]

The really MASSIVE week continues with 🚀 Llama 3.2 Release

- Introduces 1B and 3B text models for edge devices, 11B and 90B vision models
- All models support 128K token context
- 1B/3B outperform Gemma 2 2.6B and Phi 3.5-mini on key tasks
- 11B/90B vision models competitive with Claude 3 Haiku and GPT4o-mini

🛠️ Technicalities:

- Vision models use adapter layers for image-text integration
- 1B/3B models created via pruning and distillation from Llama 3.1 8B
- Post-training alignment uses SFT, rejection sampling, and DPO
- Llama Guard 3 1B reduced from 2,858 MB to 438 MB

🤝 Ecosystem:
- Day one support for Arm, MediaTek, Qualcomm
- Available on 25+ partner platforms (AWS, Azure, Google Cloud)
- New Llama Stack distributions simplify deployment

🌐 Open Source:
- Models downloadable from llama .com and Hugging Face
- Evaluated on 150+ benchmark datasets across languages

[AK @_akhaliq]

Microsoft releases GRIN😁 MoE

GRadient-INformed MoE

demo: https://t.co/DW48a5cc7D
model: https://t.co/O6nD3xreir
github: https://t.co/mcd2QF3DgA

With only 6.6B activate parameters, GRIN MoE achieves exceptionally good performance across a diverse set of tasks, particularly in coding and mathematics tasks.

[kyutai @kyutai_labs]

Today, we release several Moshi artifacts: a long technical report with all the details behind our model, weights for Moshi and its Mimi codec, along with streaming inference code in Pytorch, Rust and MLX. More details below 🧵 ⬇️
Paper: https://t.co/mMInmjiBIC
Repo: https://t.co/PFak47FMrm
HuggingFace: https://t.co/bqG4IS0ntg

[Max Schwarzer @max_a_schwarzer]

The system card (https://t.co/wM4LVBySKf) nicely showcases o1's best moments -- my favorite was when the model was asked to solve a CTF challenge, realized that the target environment was down, and then broke out of its host VM to restart it and find the flag.

[Yucheng Jiang @YuchengJiang0]

@satish1v thanks! CO-STORM will be integrated into STORM repo (https://t.co/dSPuH0CZmY) which support multiple LM and multiple retriever including vector database, which you can use your own data. We’ll release code soon. Stay tuned!

[Jorge GX @JorgeGarciaX]

@YuchengJiang0 This is super cool. Any idea when you will be open sourcing it? Looking forward to testing it!

[Vaibhav (VB) Srivastav @reach_vb]

Phi 3.5 Mini, aka smol AGI - running directly on the browser! 🔥

No setup/ install needed, just a laptop and a browser! 100% offline (once the model is loaded, it doesn't need internet)

Powered by MLC LLM - even on an old Mac like mine, it gives 20+ tok/ sec - goes up to 100 tok/ sec on a 4090/ M3 Max. ⚡

Kudos to the MLC/ web-LLM team for such a brilliant library + easy-to-use conversion.

Check out the demo below 🤗

[Jorge GX @JorgeGarciaX]

@jcontini @crewAIInc @pyautogen @llama_index @composio @langchain Also interested. I am using autogen and llamaindex for automated technology watch and podcast creation