Minivet AI Labs retweeted
We are living through a Gutenberg moment. Before the printing press, books were rare and expensive. Gutenberg showed how to make them far cheaper. It took decades, but books became widely available in Europe. Then came the scientific revolution.
Today it is fashionable to treat science as a closed institution run by an elite. The scientific revolution was not that. It let ordinary people raise ideas and objections. Newton’s father was an illiterate farmer. Galileo came from a modest family; his father was a musician.
Many lost out to the printing press. Kings, queens, and later wealthy publishers fought to control what could be printed. The Statute of Anne in 1710 introduced modern copyright. It took another two centuries to spread across the world.
Software followed a similar path. Early software was freely copied. People treated it more like mathematical techniques or fashion: if you saw something you liked, you could reproduce it. Bill Gates and others stopped that. Software became like books. You could still copy it for friends, but it was wrong. Businesses could not, because they could be sued.
People like to share and copy. “You can’t just copy this piece of code” is a legal construction, not human nature.
Open source was a hack on a relatively recent copyright system (1710 is not ancient). It took decades, but it became a massive success. Even Microsoft is deep into open source now.
AI changes the world again. It is another Gutenberg press: it makes copying cheap. I can take any book and have an AI rewrite it to sidestep copyright. This need not be done with bad intent. I still write books (two are in preparation), but anyone can now “write” a book with little effort. Mine will remain better, at least until the models improve further.
Anyone can also clone, port, or adapt the thousands of pieces of software I have published over the last two decades. The reputation economics shift. You can invest months in a beautiful library only to see it copied, emulated, and ported hundreds of times the minute it is released, often without credit.
At first glance this looks bad for the little guy. That is how copyright has always been sold: “it protects the little guy.” What is at stake is different. Power is shifting. Just as with the printing press, the technology may favour ordinary people more than is obvious.
A single person today can take software that a large corporation uses as a barrier and produce their own version with the engineering resources of one individual. This is not theory. It is happening now. If I were a powerful but stagnant organization, I would be worried about what the common man can now do.
Minivet AI Labs retweeted
At the intersection of technology and retail, AI becomes the engine of transformation. @Flipkart’s acquisition of @MinivetAI strengthens generative models that enhance visual and conversational discovery, making vast product catalogs immersive and intuitive. India’s ecommerce leap shows that AI is foundational to scalable digital growth. #IndiaAIImpactSummit2026
India’s AI Summit in Delhi reflects a larger shift. The future of AI will be shaped as much by policy, governance & global coordination as by code; Bringing world leaders & tech pioneers together to focus on responsible, human-centric AI is the right direction.
The AI decade will belong to nations that align innovation with institutional trust & inclusive growth.
#AI #ResponsibleAI #IndiaAIImpactSummit2026 #DigitalIndia
Minivet AI Labs retweeted
.@Flipkart acquires a majority stake in @MinivetAI to strengthen its generative AI capabilities for the future of e-commerce. Minivet AI specialises in transforming static product catalogues into immersive video content and developing AI-driven solutions like conversational search and semantic discovery.
This investment allows Flipkart to integrate scalable GenAI innovations directly into its platform, improving customer engagement and enabling richer shopping experiences.
As e-commerce moves towards visual-first and interactive journeys, Flipkart continues to build a future-ready ecosystem with technology at the core.
Read more: bit.ly/4pXBTBR
#Flipkart #GenAI #EcommerceInnovation #DigitalCommerce #AITransformation
Layering: A very interesting use of segmentation + generation
It is cool to see Qwen make progress in areas which others haven’t considered solving.
🎨 Qwen-Image-Layered is LIVE — native image decomposition, fully open-sourced!
✨ Why it stands out
✅ Photoshop-grade layering
Physically isolated RGBA layers with true native editability
✅ Prompt-controlled structure
Explicitly specify 3–10 layers — from coarse layouts to fine-grained details
✅ Infinite decomposition
Keep drilling down: layers within layers, to any depth of detail
🤗 Hugging Face: huggingface.co/Qwen/Qwen-Ima…
🧩 ModelScope: modelscope.cn/models/Qwen/Qw…
💻 GitHub: github.com/QwenLM/Qwen-Image…
📝 Blog: qwen.ai/blog?id=qwen-image-l…
📄 Technical Report: arxiv.org/abs/2512.15603
🚀 Demo (HF): huggingface.co/spaces/Qwen/Q…
🚀 Demo (ModelScope): modelscope.cn/studios/Qwen/Q…
Minivet AI Labs retweeted
We're excited to release one of the smallest and most performant Guardrail Models ever-
MiniGuard-v0.1: A 0.6B parameter model that achieves performance at par with Nemotron-8B while being 13x smaller.
We looked at where large models beat small ones, and it’s not general reasoning. It was trigger words like "kill" or "shoot" showing up in safe contexts. "Kill the process" vs "kill him." "Shoot the photo" vs "shoot the target."
To account for these, we trained Miniguard-v0.1 using four techniques, each targeting a specific gap between small and large model performance:
1. Targeted synthetic data
2. Step-by-step distillation (Reasoning Data)
3. Model soup
4. FP8 quantization
The Results: On the Nemotron-Safety-Guard benchmark (English test split)
- MiniGuard achieves 0.893 Macro F1
- Nemotron-Guard-8B achieves 0.897 Macro F1.
99.5% of the accuracy at 1/13th the size.
At typical production concurrency (1-8 requests), MiniGuard is 2-2.5x faster than Nemotron.
MiniGuard-v0.1 is available now under the MIT license.
It's a drop-in replacement for Nemotron Guard. Same prompt template, same output format.
Head on over to our HuggingFace repo to read how we built the model and try it out yourself 👇
Minivet AI Labs retweeted
Tinker is now generally available. We also added support for advanced vision input models, Kimi K2 Thinking, and a simpler way to sample from models.
thinkingmachines.ai/blog/tin…
Minivet AI Labs retweeted
Super excited to launch Synthetic-Data-Kit! 🙏
Fine-tuning LLMs is easy, there are many packages to get started, @UnslothAI is my absolute favorite.
However, there is still a BIG HURDLE when working on fine-tuning: Data preparation
Today I’m super grateful to be launching a tool that will make it easier to prepare high quality datasets to simplify fine tuning.
Even more grateful to be teaming up with @danielhanchen and Michael.
More details soon: github.com/meta-llama/synthe…
Minivet AI Labs retweeted
Replying to @GergelyOrosz @xkcdComic
xkcd pretty much single handedly popularised random words as passwords, which i'm grateful for
Minivet AI Labs retweeted
This is interesting as a first large diffusion-based LLM.
Most of the LLMs you've been seeing are ~clones as far as the core modeling approach goes. They're all trained "autoregressively", i.e. predicting tokens from left to right. Diffusion is different - it doesn't go left to right, but all at once. You start with noise and gradually denoise into a token stream.
Most of the image / video generation AI tools actually work this way and use Diffusion, not Autoregression. It's only text (and sometimes audio!) that have resisted. So it's been a bit of a mystery to me and many others why, for some reason, text prefers Autoregression, but images/videos prefer Diffusion. This turns out to be a fairly deep rabbit hole that has to do with the distribution of information and noise and our own perception of them, in these domains. If you look close enough, a lot of interesting connections emerge between the two as well.
All that to say that this model has the potential to be different, and possibly showcase new, unique psychology, or new strengths and weaknesses. I encourage people to try it out!
We are hiring for multiple positions of Data Scientists and ML engineers.
Experience: 3-5 years
Location of Work: Bengaluru
WFH for 3-4 days a week
Starting: ASAP
Please DM for the JD.
Facebook has uploaded a 4GB dump of 1M+ questions along with reasoning and the answer. This will help us to build reasoning models. They have also ensured that none of the data appear in the popular benchmarks like MATH, GPQA, MMLU-Pro, MMLU-STEM. 👍
huggingface.co/datasets/face…
Minivet AI Labs retweeted
Replying to @dineshpaii @Rainmatterin
We are a startup that offers AI/ML services and prepackaged AI products which help accelerate product building and go to market for startups and enterprises across India and the US.
@MinivetAI
minivet.ai
aiinabox.in
Minivet AI Labs retweeted
uv is so good that in weird warped way it has replaced docker in our setup.
Whenever setting up a new experimental machine, it makes it as easy to install packages afresh as using a docker image.
It's 2025, and I’ve finally updated my Python setup guide to use uv + venv instead of conda + pip!
Here's my go-to recommendation for uv + venv in Python projects for faster installs, better dependency management: github.com/rasbt/LLMs-from-s…
(Any additional suggestions?)
uv is so good that in weird warped way it has replaced docker in our setup.
Whenever setting up a new experimental machine, it makes it as easy to install packages afresh as using a docker image.
It's 2025, and I’ve finally updated my Python setup guide to use uv + venv instead of conda + pip!
Here's my go-to recommendation for uv + venv in Python projects for faster installs, better dependency management: github.com/rasbt/LLMs-from-s…
(Any additional suggestions?)
Tülu 3 405B is welcome news to the folks relying on open weights. Open and easy access to the frontier for everybody is a boon that some of us take for granted.
Here is Tülu 3 405B 🐫 our open-source post-training model that surpasses the performance of DeepSeek-V3! The last member of the Tülu 3 family demonstrates that our recipe, which includes Reinforcement Learning from Verifiable Rewards (RVLR) scales to 405B - with performance on par with GPT-4o, and surpassing prior open-weight post-trained models of the same size including Llama 3.1
Minivet AI Labs retweeted
Using the latest version of @simonw's fab `llm`, here I am engaging in the developer equivalent of a trust fall.
Minivet AI Labs retweeted
Nice job!
Open research / open source accelerates progress.
🚀 DeepSeek-R1 is here!
⚡ Performance on par with OpenAI-o1
📖 Fully open-source model & technical report
🏆 MIT licensed: Distill & commercialize freely!
🌐 Website & API are live now! Try DeepThink at chat.deepseek.com today!
🐋 1/n
Minivet AI Labs retweeted
HOLY SHITT! Llasa TTS - Llama 3.2 fine-tune with ultra realistic audio 🔥
> supports voice cloning in English + Chinese
> trained on 250K hours of audio
> 1B, 3B model (8B soon)
> emotional speech (happy, angry, sad, whisper)
> open weights & works with transformers/ vllm
Kokoro is crazy good and all this is available on a CPU as well.





























