Dmitry Noranovich

Machine Learning Engineer. I learn by teaching. Opinions are my own.

Building

[Google AI Studio @GoogleAIStudio]

introducing Gemini 4 Hydrogen

this new model brings cutting-edge intelligence to everyone — with speed and efficiency for everyday coding and agentic workflows

start building with Hydrogen via the Gemini API and in AI Studio: https://t.co/Vzi1BPlsdz

[Dmitry Noranovich @javaeeeee1]

Read Less, Solve More: Token-Efficient Sparse Reading of External Content for AI Agents

https://t.co/HzdsIbhkHH

[DeepLearning.AI @DeepLearningAI]

Make predictions on tabular data without training a new model for each dataset.

In Tabular Foundation Models for Data Science Agents, built with @prior_labs, you'll give an AI agent a tabular foundation model as a prediction tool.

Enroll for free: https://t.co/hOsNojpvnT

[Dmitry Noranovich @javaeeeee1]

ElevenLabs: Create natural AI voices instantly in any language by @NevFlynn https://t.co/8MR6xk61lK

[Dmitry Noranovich @javaeeeee1]

MACaQ: Fast, Model Agnostic Calibrated Quantization for Low-Precision Weights and Activations

https://t.co/UM38zQxMUn

[Google Antigravity @antigravity]

Announcing /swarm, a new slash command in Google Antigravity for orchestrating specialized agents in parallel across the desktop app and CLI.

Turn a single prompt into a coordinated multi-agent workflow where agents plan, build, and verify together on a live message board, with the flexibility to mix and match different models for each role.

[Hugging Face @huggingface]

Local AI 301: Quantization https://t.co/KQdi4h2HNt

[Google DeepMind @GoogleDeepMind]

Gemini Robotics 2.5 is here. 🤖

Our latest physical AI is able to learn hundreds of everyday skills - from folding laundry and serving pastries to autonomously sorting car parts on the factory floor.

Here’s a snapshot of its capabilities 🧵

[Dmitry Noranovich @javaeeeee1]

Operum: Your AI product team, with expert workflows built in by @alprimak and @obprimak https://t.co/0r6cPgnSey

[Dmitry Noranovich @javaeeeee1]

Levo: Your AI agent finds what's safe to delete on your Mac by @CristianRus4 https://t.co/36Q47kyNVy

[Dmitry Noranovich @javaeeeee1]

Almanzor: Ask AI + MCP server for your docs, with cited answers by @victorelexpe https://t.co/9XGFlqGuim

[Dmitry Noranovich @javaeeeee1]

ReproBridge: Building Reproducibility into ML Research Artifacts

https://t.co/9MViKlwWOG

[Claude @claudeai]

During pre-release testing, Claude Fable 5.2 and Claude Mythos 5.2 worked on ambitious projects in many different fields. Here are a few.

From a one-paragraph prompt, Fable 5.2 designed three working electronic devices, including this home voice assistant hub.

[Connor Shorten @CShorten30]

What do Rerankers and Chess have in common? ♟️

I am SUPER EXCITED to publish the 150th episode of the Weaviate Podcast featuring Ghita Houir Alami (@ghita__ha), Co-Founder of ZeroEntropy and now leading Model Research at Notion! 🔥

This is one of my favorite deep dive podcasts into Search! 🔎

We explored how pairwise preferences can be turned into useful pointwise scores with ZeroEntropy's zELO method! Like chess Elo, a sparse sample of pairwise battles between documents is enough to fit an absolute score through a Thurstone model, and that signal is then distilled into a pointwise reranker. The result... 🥁 ... the state-of-the-art zerank-2 cross encoder! 🔥

The podcast covers a lot of interesting topics in Search! I loved learning about Reranking with Jev / Decision Models. 🛠️

Ghita explained how many teams want cross encoders to prune and classify rather than just order results. Calibrated relevance scores can support searching with a Dynamic K, thresholding, and classification. 🧠

We also discussed the tradeoffs between pointwise, pairwise, and listwise ranking. ZeroEntropy's pairwise reranker was slightly more accurate, but distilling it into a pointwise model came close to matching it at much lower inference cost. ⚡️

We also covered infrastructure for rerankers, agentic search and reranking, instruction-following rerankers, metadata in training rerankers, and the relationship between RSI and SLMs! 📜

This was a super fun conversation, and I hope you find it useful! 💚

YouTube: https://t.co/rpWZk8saLg
Spotify: https://t.co/qUJVYoa3TK

[Claude @claudeai]

Introducing Claude Fable 5.2 and Claude Mythos 5.2, designed to tackle the most daunting technical challenges.

They’re capable collaborators on ambitious projects in many fields, and show improvements in areas like coding, reasoning, and vision.

[Dmitry Noranovich @javaeeeee1]

Databox: Agentic analytics platform for you and your agents. by @davorin and @pc4media https://t.co/mADJvyJFPf

[Dmitry Noranovich @javaeeeee1]

Dageno AI: Become the brand AI answers recommend across 7+ major LLMs. by @tim_geo_seo https://t.co/2hv2VasEaX

[Dmitry Noranovich @javaeeeee1]

Beneath the Surface: What We Overlook When We Talk About Agent Evolution

https://t.co/8ffYVLLIbh

[Dmitry Noranovich @javaeeeee1]

The Edge Advantage: Building Fast, Scalable, and Intelligent Apps on Cloudflare’s Developer Platform

https://t.co/wN4zO7kCMB

[Dmitry Noranovich @javaeeeee1]

@addyosmani Discuss the article on Reddit https://t.co/15JoXvf2Gj