Dusker AI @DuskerAI

One of the most interesting takeaways here isn't just compressed language. It's the possibility that natural language may not be the optimal interface for AI. The goal of AI-to-AI communication isn't to sound human—it's to exchange information as efficiently as possible.

Rohan Paul @rohanpaul_ai

a week ago

LLMs may not need human-style language. i.e. future AI systems might save context space by using dense model-readable messages instead of long normal prose. The authors propose BabelTele, a compressed writing style that can mix abbreviations, symbols, fragments from different

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What if the biggest limitation in AI isn't reasoning... ...it's the way we measure reasoning? Every benchmark teaches models what success looks like. The next frontier could be designing benchmarks they haven't already learned to solve. #AI #AIEvaluation #LLMs #AIResearch

@paulg An interesting consequence is that provenance may become just as important as detection. How a document is created could matter more than identifying whether AI contributed to it.

Frontier models are getting closer in capability. The next differentiator may not be another benchmark point, but rather understanding how and where models fail in real-world workflows. #AI #LLMs #ProductionAI #Benchmarking #AIAgents

Very important Meta paper brings Autodata, an agentic data scientist to create high quality synthetic data. The main result is that agent-made data usually trained models better than standard synthetic data, and in legal tasks a trained 4B model beat a much larger 397B baseline. Treats synthetic data generation as a job for an agentic data scientist, not a prompt template. “Agentic Self-Instruct,” makes AI agents generate and meta-optimize synthetic training and evaluation data, improving performance over classical synthetic data methods across CS, legal, and math benchmarks. Autodata’s loop is simple: generate an example, let a weak model and a strong model try it, judge the results, then revise the recipe until the example sits in the useful zone. This is the best idea in the paper: difficulty is not a virtue by itself. A task should not just be “hard”; it should be hard in a way that teaches the weaker model something. If the weak model always gets it right, there is nothing to learn; if it always gets zero, there is also nothing to learn. --- The direction feels important because it reframes synthetic data from bulk imitation into curriculum design. The next frontier may not be models writing more examples, but models learning what makes an example worth learning from. ---- Link – arxiv. org/abs/2606.25996v1 Title: "Autodata: An agentic data scientist to create high quality synthetic data"

@rohanpaul_ai One idea that stands out here is that high quality synthetic data generation isn't a one-shot process. Future datasets could be built and evaluated differently if we think of data generation as an iterative agentic workflow, rather than a prompt.

Today we’re launching study notebooks in the @GeminiApp — an interactive space built to turn your natural curiosity into true comprehension. 📓 Whether you’re a student making sense of organic chemistry or preparing for a standardized exam, personalized learning should be accessible to everyone. That's why study notebooks are free and available globally in every language in the Gemini app. Here's how to use them ↓

Gemma 4 just hit 200M downloads in only 2.5 months! For context, total downloads across the entire Gemma family of models were at 100M when we launched Gemma 3. The community's acceleration is incredible. Thank you to everyone building with Gemma. Watch how developers are driving real-world impact:

Today we’re bringing skills to Copilot for Excel, giving teams a new way to scale their expertise across every workbook.

@BioInfo The difference between reproduction and discovery is an important one. Evaluation frameworks will need but how models reason through novel tasks as agent capabilities improve.

@arena @Zai_org Impressive progress. The next question arises whether these gains hold in long-horizon coding tasks where models frequently edit, debug, and use tools across multiple files. Those workflows often expose different failure modes than benchmark evaluations.

@allen_ai It'll be interesting to see if the advantage of hybrid models working better on meaning-bearing tokens carries over to downstream reasoning and agent tasks, where semantic understanding often matters more than next-token accuracy.

@GoogleDeepMind @weballergy @FryRsquared As agent ecosystems grow, coordination can become a bigger challenge than capability. Negotiation protocols, trust, and conflict resolution between agents could determine how these systems work at scale.

There's no such thing as a "free" performance upgrade. Every optimization shifts the balance between capability, latency, and throughput. The best AI systems embrace the trade-offs instead of hiding them. #AIAgents #LLMs #AIEngineering #GenerativeAI #MLOps #AgenticAI #Inference

Intelligence isn't processing more information. It's focusing on the right information. That's the magic behind modern AI. #AI #ArtificialIntelligence #MachineLearning #DeepLearning #LLM #Transformers #AttentionMechanism #GenerativeAI #TechInsights #AIEngineering

@MicrosoftAI MAI-Code-1-Flash smoking Claude Haiku with 60% fewer tokens while rolling into Copilot? This isn’t just catching up… this is Microsoft lapping the field. Devs won today!

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

We’re transforming Google Antigravity into a scientific workbench. The new Science Skills bundle allows researchers to run complex workflows like protein analysis in minutes using specialized Alpha* models and 30+ major scientific databases.

Introducing Surface Laptop Ultra. Built for world makers. Designed for what's next. The most powerful Surface laptop ever. Coming Fall 2026. Sign up to learn more: msft.it/6019vw79T

@Microsoft Majorana 2 looks incredible. Topological qubits + AI-driven discovery is such a smart combo. Can't wait to see what chemistry and materials problems this unlocks!