Lucas Brusasca

AI Solutions Engineer | Applied AI and data for business | MSc in Data Science candidate — Universidad Austral

[Anthropic @AnthropicAI]

New on our Engineering blog: Creating in-house source control.

In May, we merged around 2,500 code changes per day. In September, we merged more than 30,000 per day. As a result, we became bottlenecked by developer infrastructure built for human (rather than agent) scale.

Here, we share how we built a new version control system—and fully migrated our monorepo from git—in five months: https://t.co/wzNkwHs7IN

[Sundar Pichai @sundarpichai]

Gemini 4 Argon is here.

It’s available globally today for developers and enterprises in the paid tier of the Gemini API and the Gemini platform, for Ultra subscribers in Google Antigravity, and Ultra and Pro subscribers in the Gemini app and Search.

Hope you all enjoy it!

[Cursor @cursor_ai]

Cursor is now part of The X Subscription.

Current Cursor subscribers can keep their plan or switch to The X Subscription.

Paid plans share one usage pool across Cursor, Grok Build, and other Grok products, with twice the coding usage on Grok models as the equivalent Cursor plan. Premium, Super, and Ultra also include Grok Bot.

To make room for more Grok usage, third-party models in Cursor move to API pricing on the new plans. We'll keep working to make your usage go further, with more compute, better inference, and more token-efficient models.

The X Subscription is available at https://t.co/KIM9X7FZrq.

[Mark Zuckerberg @finkd]

Introducing Muse Wonder, our new family of frontier models with state of the art performance for personal and professional agentic work.

[SpaceX Super Intelligence @SpaceXSI]

We've unified plans across Grok, Cursor, and X Premium.

Introducing The X Subscription.

[Sakana AI @SakanaAILabs]

Introducing Multi-Agent Self-Supervision (MASS): Recursive self-improvement through collective intelligence

Blog: https://t.co/LkQe5k7jOW

As AI systems tackle increasingly open-ended research problems, human experts may struggle to assess results and guide further progress. The model itself may be the best available optimizer and evaluator. The challenge is obtaining useful self-supervision within that homogeneous loop, where a model risks reinforcing its own blind spots rather than correcting them.

We ask whether a model can learn from the collective intelligence of its own team.

We introduce Multi-Agent Self-Supervision (MASS). One shared model solves tasks through a team of virtual subagents, searches for better multi-agent workflows, then uses its own judgments to select the best ones.

Training on the best team’s executions provides self-supervision by distilling the team’s collective experience into the shared model.

We ran two cycles using a 27B open-weights model on synthetic open-ended research tasks. After the second cycle, score per output token reached 1.2-1.6x the base model's level across four research benchmarks.

Three key results:

First, MASS enables role generalization within an RSI loop: training only on task-solving data from the optimizee naturally improves the capabilities of both the optimizer and the evaluator.

Second, multi-agent trajectories are more efficient training data than single-agent trajectories for RSI.

Third, optimizer capacity, rather than evaluator capacity, can be the bottleneck in the speed of improvement.

We believe the community needs to investigate homogeneous RSI carefully to support AI safety. When a model is also its own evaluator, shared blind spots could allow mistakes to be accepted as progress and reinforced through training. For AI to improve beyond human expertise safely, we need to understand these failures and preserve effective ways to detect them and intervene.

Paper: https://t.co/SzXDlAzrUO
Code: https://t.co/xc870ZHRgh

This work was a collaboration with researchers at UC Berkeley.

[Lucas Brusasca @LucasBrusasca]

@MathiasChu Excelente 👏🏻
Me interesa.

[Jorge @tebayoso]

Ser buen programador/dev/ingeniero hoy es estar todo el día en Twitter.

[Brais Moure @MoureDev]

¿Quieres diseñar diagramas profesionales desde tu agente de IA?

Diagram Design te permite crear 42 tipos diferentes con HTML + SVG utilizando Claude Code, Codex, Copilot, Pi... Todo manteniendo los contenidos uniformes y sin distorsión.

→ https://t.co/jcHqie4mLa

[Nous Research @NousResearch]

Hermes Agent is now live (and featured) in the @Microsoft Store for seamless one-click install.

More updates for the Windows ecosystem coming soon!

[Claude @claudeai]

Claude Dashboards and Claude Motion are in beta today.

Ask Claude to turn your data into live dashboards and your ideas into animated explainers.

[Andrej Karpathy @karpathy]

We'll be spending a lot more time trying to understand the outputs of language models. A few thoughts, tips & tricks:

Writing. Something I've had success with: Ask your LLM to explain something in ASD-STE100, it's a controlled language specification originally developed for aerospace maintenance documentation. LLMs well-versed in this language and it comes with heavy constraints on clean writing style that I often find a lot more readable. Sometimes I've tried to soften it a bit e.g. ask for "80% of the way to ASD-STE100" because the spec is quite stringent. But even better:

Diagrams / images. Instead of writing, ask your LLM to create a diagram. These can be a lot easier to process, parse, and understand. But even better:

Web pages. Ask for output "in HTML" to get a beautiful, interactive webpage. LLMs are getting really good at frontend and can create beautiful experiences, animations, etc. But even better:

Explainer videos. The output format I am most bullish on is fully custom / bespoke explainer videos generated on any arbitrary topic. Experiment with things like "Create a 3b1b style video explainer on X. Use my ElevenLabs API key for audio narration". (you'd need an API key for the latter or you can ask your LLM to find you decent free alternatives that use your local compute). This is actually starting to work!

In summary:
- As LLMs get better, they will do more and more of the legwork autonomously, and a lot more of our work will rise up the abstractions into oversight and understanding.
- Luckily, LLMs can help here too because as intelligence and code are increasingly abundant, you can ask for large, custom, discardable software artifacts (e.g. web apps, video explainers) that would have never made sense to create before. Push the boundaries here and you'll be surprised.

[The White House @WhiteHouse]

Official.

[santi @santtiagom_]

el mismo día tenés a Anthropic lanzando Opus 5.5 y a OpenAI respondiendo horas después con GPT-6 Sol y Luna.

2 de los labs más importantes del mundo moviendo ficha casi al mismo tiempo.

hermoso momento para estar vivo

[Anthropic @AnthropicAI]

Claude Opus 5.5 is available today.

[Sam Altman @sama]

GPT-6 Sol and Luna are great models but also these characters are so cute

[SpaceX Super Intelligence @SpaceXSI]

Grok 4.7 is here.

It's a notable improvement over Grok 4.6 at the same price and speed.

[TypeSafe AI @typesafeai]

Jev is now available to everyone. No waitlist.
Start using it here: https://t.co/5MfN4rIif5