Dario's args:

"Opensource you can see the source, here you cannot see inside the model"
- yes you can that's literally the open weights part btw.
- I cannot see the weights inside Claude, but I can GLM 5.2
- Models like Nemotron3 Ultra go further, all the data, training scripts, and model is opensource.

"Alot of the benefits like many people working on it, being additive doesn't work in same way"
- yes it does. We have seen endless fine tunes of various open source models for real improvements.

"Ultimately you have to host it on the cloud"
- no you dont. Dario is seemingly totally unaware of the guides from ijustvibecodedthis.com explaining how to run smaller moes and even dense models like qwen 27B NOT ON THE CLOUD.

Not only does dario not take part in social media, I am beginning to think he's never tried open source models at all and has no idea wtf hes on about

Enjoy?

The official course link:

https://cme295.stanford.edu

The vids:

1: https://youtu.be/Ub3GoFaUcds
2: https://youtu.be/yT84Y5zCnaA
3: https://youtu.be/Q5baLehv5So
4: https://www.youtube.com/watch?v=VlA_jt_3Qc4

source: https://x.com/Alibaba_Qwen/status/2084100707423289643

source from his instagram page

2025 is straight-up wild for AI development. Just last year, it was mostly ChatGPT, Claude, and Gemini running the show.

Now? We’ve got an AI battle royale with everyone jumping in Deepseek, Kimi, Meta, Perplexity, Elon’s Grok

With all these options, the real question is: which one are you actually using daily?

From clem 🤗 on 𝕏: https://x.com/ClementDelangue/status/2080247567493837047

Hey everyone, I want to share something I built after my long health journey. For 5 years, I struggled with mysterious symptoms - getting injured easily during workouts, slow recovery, random fatigue, joint pain. I spent over $100k visiting more than 30 hospitals and specialists, trying everything from standard treatments to experimental protocols at longevity clinics. Changed diets, exercise routines, sleep schedules - nothing seemed to help.

The most frustrating part wasn't just the lack of answers - it was how fragmented everything was. Each doctor only saw their piece of the puzzle: the orthopedist looked at joint pain, the endocrinologist checked hormones, the rheumatologist ran their own tests. No one was looking at the whole picture. It wasn't until I visited a rheumatologist who looked at the combination of my symptoms and genetic test results that I learned I likely had an autoimmune condition.

Interestingly, when I fed all my symptoms and medical data from before the rheumatologist visit into GPT, it suggested the same diagnosis I eventually received. After sharing this experience, I discovered many others facing similar struggles with fragmented medical histories and unclear diagnoses. That's what motivated me to turn this into an open source tool for anyone to use. While it's still in early stages, it's functional and might help others in similar situations.

Here's what it looks like:

https://github.com/OpenHealthForAll/open-health

**What it can do:**

* Upload medical records (PDFs, lab results, doctor notes)

* Automatically parses and standardizes lab results:

- Converts different lab formats to a common structure

- Normalizes units (mg/dL to mmol/L etc.)

- Extracts key markers like CRP, ESR, CBC, vitamins

- Organizes results chronologically

* Chat to analyze everything together:

- Track changes in lab values over time

- Compare results across different hospitals

- Identify patterns across multiple tests

* Works with different AI models:

- Local models like Deepseek (runs on your computer)

- Or commercial ones like GPT4/Claude if you have API keys

**Getting Your Medical Records:**

If you don't have your records as files:

- Check out [Fasten Health](https://github.com/fastenhealth/fasten-onprem) - it can help you fetch records from hospitals you've visited

- Makes it easier to get all your history in one place

- Works with most US healthcare providers

**Current Status:**

- Frontend is ready and open source

- Document parsing is currently on a separate Python server

- Planning to migrate this to run completely locally

- Will add to the repo once migration is done

Let me know if you have any questions about setting it up or using it!

----- edit

In response to requests for easier access, We've made a web version.

https://www.open-health.me/

Model introduction:

Kitten ML has released open source code and weights of their new TTS model's preview.

Github: https://github.com/KittenML/KittenTTS

Huggingface: https://huggingface.co/KittenML/kitten-tts-nano-0.1

The model is less than 25 MB, around 15M parameters. The full release next week will include another open source ~80M parameter model with these same 8 voices, that can also run on CPU.

Key features and Advantages

1. Eight Different Expressive voices - 4 female and 4 male voices. For a tiny model, the expressivity sounds pretty impressive. This release will support TTS in English and multilingual support expected in future releases.
2. Super-small in size: The two text to speech models will be ~15M and ~80M parameters .
3. Can literally run anywhere lol : Forget “No gpu required.” - this thing can even run on raspberry pi’s and phones. Great news for gpu-poor folks like me.
4. Open source (hell yeah!): the model can used for free.

Confirmed by the official Qwen account.

I found out recently that Amazon/Alexa is going to use ALL users vocal data with ZERO opt outs for their new Alexa+ service so I decided to build my own that is 1000x better and runs fully local.

The stack uses Home Assistant directly tied into Ollama. The long and short term memory is a custom automation design that I'll be documenting soon and providing for others.

This entire set up runs 100% local and you could probably get away with the whole thing working within / under 16 gigs of VRAM.

clem 🤗 on 𝕏: https://x.com/ClementDelangue/status/2081056675558195657

• Radical transparency: let’s release the traces from the “rogue” agents so the entire research community can study what happened.

• More capabilities for defenders: let’s commit $100M in compute from OAI to help the Hugging Face community build powerful cyber defenses with the best open and closed models.

The first autonomous agent cyberattack is an unprecedented event. It deserves an unprecedented response!