Michael Jentsch

Weeks of coding can save you hours of planning.

[Google Research @GoogleResearch]

🚀 Introducing Gemini-SQL2, our breakthrough text-to-SQL capability powered by Gemini 3.1 Pro! We've achieved state-of-the-art results on the highly competitive BIRD benchmark, translating natural language into execution-ready SQL queries. 🧵👇

[Artificial Analysis @ArtificialAnlys]

Cohere just released North Mini Code, a small 30B parameter (3B active) open weights coding model that scores 27.6 on the Artificial Analysis Intelligence Index

Less than a month since @cohere's last model release, Command A+, has launched another open weights model that is optimized for coding, and much smaller at 30B total parameters and 3B active parameters.

Key Takeaways:

➤ Achieves 27.6 on the Artificial Analysis Intelligence Index, above gpt-oss-20B (high) at 24.5 and just below Mistral Small 4 (119B parameters, 6.5B active) at 27.8

➤ Scores competitively on the Artificial Analysis Coding Index (weighted average of Terminal-Bench Hard and SciCode) against open weights models in its size class, scoring 33.4, significantly above GLM-4.7-Flash at 25.9, and below Qwen3.6 35B A3B at 35.2. However, it underperforms on non-coding agentic tasks, scoring 14% on GDPval-AA and 37% on 𝜏²-Bench Telecom

➤ On Cohere’s API, North Mini Code is faster than several comparable open weights models of its intelligence and size class (~199 output tokens per second)

➤ North Mini Code is a text-only 30B total parameter and 3B active parameter model, and is open-sourced under the Apache 2.0 license

[Google Gemma @googlegemma]

Building super fast experiences with Gemma just got easier.

Gemma 4 MTP is now officially merged into llama.cpp. Developers can now pair MTP with Gemma 4 QAT for a fast, lightweight setup.

[freeCodeCamp.org @freeCodeCamp]

Building a custom chatbot can be tricky, especially if you want to keep the info private & efficient.

So here, @atuo_elabonga teaches you how to create a local Retrieval-Augmented Generation application using Ollama & ChromaDB in R.

By the end, you'll have a Shiny interface for your chatbot that fetches relevant info while staying private.

https://t.co/w95H9sYioD

[Philipp Schmid @_philschmid]

More Gemma 4! New QAT Gemma 4 checkpoints with similar performance while using ~4x less memory!

It comes with a new mobile quantization format that reduces memory footprint of Gemma 4 E2B to just 1GB.

Quantization-Aware Training (QAT) simulates low-precision operations during training to allow loss-less quantization afterwards for smaller, faster models while maintaining accuracy.

Available on @huggingface and directly runnable.

[Artificial Analysis @ArtificialAnlys]

Google’s newly released open weights model, Gemma 4 12B, supports transcription but is far from the frontier, scoring 8.8% on AA-WER (#58)

Gemma 4 12B is the latest release from @GoogleDeepMind in the Gemma 4 family. With a score of 8.8% on AA-WER, it is able to capture a reasonable amount of conversation context, but underperforms compared to transcription-focused open weights models like Voxtral Mini Transcribe 2 (3.6% WER, with 4B parameters) and slightly larger open weights language models like Voxtral Small (2.8% WER, with 12B parameters). The new model launched alongside their local dictation app, Eloquent, available on MacOS and iOS.

Gemma 4 12B is the largest in the Gemma 4 family to support transcription, alongside Gemma 4 E4B and Gemma 4 E2B, with Gemma 4 31B and Gemma 4 26B A4B supporting text, image and video input only. These models are available on a variety of platforms including Hugging Face, Ollama and LMStudio.

We are currently running Gemma 4 12B through the full Artificial Analysis Intelligence Index and will share results soon.

[Flutter @FlutterDev]

LiteRT-LM support for Flutter is coming soon to the flutter_gemma package 💖

This will enable you to run powerful on-device AI models like Gemma 4 across devices, all while helping to ensure peak performance with GPU and NPU acceleration across all of Flutter’s stable platforms.

[Lisan al Gaib @scaling01]

MiniMax-M3 is now the best open-source model on the AAI

It's ahead of Kimi-K2.6, GLM-5.1 and DeepSeek-V4-Pro

On the Coding Index it's slightly behind V4-Pro and K2.6, but it's beating them on the Agentic Index

CritPt scores are a bit worrisome.

But the biggest highlight here is probably its reasoning efficiency compared to other open models.
(but still miles behind closed models)

[TonoKen3🤖Local LLM&Infrastructure Architect🚀とのけん @Tono_Ken3]

むむ？この4Bモデルのスコアの跳ね方が異常。試す

https://t.co/G6VIn63nUg

まさかQwen3.5−397b−a17bに匹敵するなぞ

[BosonAI @boson_ai]

Higgs Audio v3 TTS is here.

Built for voice AI that speaks, not just reads:
• 100 languages with single-digit WER/CER
• inline control over emotion, style, prosody, and sound effects
• API, Workspace, and open weights
• Blog 👉 https://t.co/C8frDlfO5D

Watch the demo 👇

[PyQuant News 🐍 @pyquantnews]

Key features:

• Free historical data
• Includes walk forward analysis
• A super-fast backtesting engine
• Create and execute trading rules across different assets

Get it here:

https://t.co/UxIYOTosfE

[Google Gemma @googlegemma]

Meet Gemma 4 12B!

A unified, encoder-free multimodal model designed to bring high-performance intelligence directly to your laptop, and released under an Apache 2.0 license.

Bridging the gap between edge efficiency and advanced reasoning. Here is what’s new with Gemma 4 12B: 👇

[Cliff Pickover @pickover]

Dinosaurs: Surrealism, Renaissance, Cubism, and more....

By Noemi Fadda, Used with permission, https://t.co/H7DYPZk2V7

[CREALITY Official @Creality3dP]

The Twelve Years of Creality: AI Ecosystem

12 years of creating with Makers worldwide — thank you for being part of the journey.

Today is Filament Explorer Day

For Makers who love testing materials, fine-tuning settings, and discovering the full potential of every filament, creativity starts before the print. With the Creality ecosystem, every spool becomes a tool for turning ideas into reality.

To celebrate our passionate Filament Makers, we’ve prepared a special giveaway.

Giveaway Prize: Choose Any 4 Rolls of Creality Filament

How to Enter:
Follow @crealityfilament & @Creality3dP
Like this post
Share this post to your story for a higher chance to win
Winners will be selected across all social media platforms on June 8th.
Discover More: https://t.co/UI2WCkOdov

Thank you for creating with Creality — and cheers to the next chapter of making together.

#12YearsOfCreality #MakeWithCreality #crealityfilament #filament #3DPrinting

[Steven Cheng @stevencheng]

The World's full open source Smallest Desktop-Scale Two-Wheeled Robot.
Github: MuShibo/Micro-Wheeled_leg-Robot

[David Hendrickson @TeksEdge]

🌞This is big Local AI news! A new open-source Computer-Use LLM has just launched.

Holo 3.1 is H Company’s (🇫🇷) new local computer-use agent model that beats Qwen3.5-397B, Kimi-K2.5, and Sonnet 4.6!

Since it is built for local deployment →
⬩ Runs fully on your machine (MacBook, Windows PC, DGX Spark, RTX Spark)
⬩ Based on Qwen architecture, specialized for GUI understanding & computer control
⬩ Optimized checkpoints: NVFP4, FP8 & Q4 GGUF (0.8B to 35B sizes)
⬩ Strong gains: 79.3% on AndroidWorld benchmark (35B model)

💻 Comparison to Qwen3.5:
Holo 3.1 is fine-tuned specifically for computer-use agents (screen understanding, planning, clicking, navigation). Better at real GUI tasks than general-purpose Qwen3.5, especially when running locally.⚡

[stevibe @stevibe]

Qwen3.6 35B A3B can't fill out a paper form on its own. But give it NVIDIA's LocateAnything-3B — the #1 trending model on HuggingFace — as its eyes, and the two small models get it done together.

(The test: place each element at the right pixel position on a blank form image, not type into a field.)

Setup:
> Qwen is the brain (main model), LocateAnything is the eyes (helper model acting as a tool).
> I gave Qwen a new tool: ask "where's the email field?" and LocateAnything returns the exact x, y, width, height.
> The blue boxes on the screen are its detections. Look how tight they are — it nails every field.

Result:
> Qwen3.6 35B A3B + LocateAnything-3B: form completed, all info correct.
> Name, DOB, ID, gender, marital status, nationality, email, phone, address, postal code: all landed in the right field areas.
> Character-box alignment still a touch loose, but every value is where it belongs.
> 9m10s, 224.5k input, 24.3k output, 21 turns.

Why it matters:
> Qwen alone can't finish this test. Bolt on a 3B model that does exactly one thing > locate > and suddenly it can.
> A combination of small models can do the work of a single large one.

[MOSI @MosiAI_Official]

MOSS-TTS-v1.5 just reached #1 on Hugging Face Trending for Text-to-Speech, with 20.6K downloads.

A multilingual, controllable TTS model with stable voice cloning, long-form generation, and precise pause control.

MOSS-TTS-v1.5 is now officially supported by vLLM-Omni and SGLang-Omni.

Built by OpenMOSS-Team.

Try it:
GitHub: https://t.co/mSlALD6Fzy
Hugging Face: https://t.co/qTv7xu1MZ5
ModelScope: https://t.co/NzAXgAzagL

[Microsoft AI @MicrosoftAI]

Seven new models launching at Build: let’s go!
Reasoning. Code. Image. Transcribe. Voice.

Built from scratch on a clean data lineage, designed for efficiency, working seamlessly as a family of models

Thread 🧵
#MSBuild

[Mark Pors 🦖 @pors]

Most neural nets are still based on the model of a neuron as proposed in the 1950's: u = activation(w·x + b)

In a new paper, researchers propose a more accurate model of a biological brain neuron and found that it has quite a few advantages, like needing less training data.