Curious

Technology enthusiast, thinker , Security engineer

[Curious @PUK_BLACK]

@JacoVerster @elonmusk @aaronburnett Fly to farther stars✨

[Claude @claudeai]

Introducing Claude Science, a new app designed with every stage of research in mind.

Artifacts traced to their code, environments managed on demand, and 60+ optional scientific databases that you can connect.

Available now in beta.

[Curious @PUK_BLACK]

Rocket Lab $RKLB 刚刚宣布，它已同意以大约80亿美元的现金和股票交易收购 Iridium $IRDM

[Evan @StockMKTNewz]

Rocket Lab $RKLB just announced that it has agreed to acquire Iridium $IRDM in a roughly $8 Billion cash-and-stock deal

[日经中文网/日經中文網 @rijingzhongwen]

【本田首次出現營業虧損,2025財年虧4000億日元】上一財年為盈利1.2134萬億日元，這也是本田上市以來首次���現營業虧損……
https://t.co/wsJPgylwwy

[Curious @PUK_BLACK]

@dahulilang @fxtrader 请看官方的blog 并不只是50美元

[Curious @PUK_BLACK]

@Alibaba_Qwen Congratulation👏👏

[Chaofan Shou @shoucccc]

Claude code source code has been leaked via a map file in their npm registry!

Code: https://t.co/jBiMoOzt8G

[Google Research @GoogleResearch]

Introducing TurboQuant: Our new compression algorithm that reduces LLM key-value cache memory by at least 6x and delivers up to 8x speedup, all with zero accuracy loss, redefining AI efficiency. Read the blog to learn how it achieves these results: https://t.co/CDSQ8HpZoc

[Thariq @trq212]

We just released Claude Code channels, which allows you to control your Claude Code session through select MCPs, starting with Telegram and Discord.

Use this to message Claude Code directly from your phone.

[DailyPapers @HuggingPapers]

Introducing the Daily Papers SKILL.md

Enables agents to
> read paper content as markdown
> search papers
> find linked @huggingface models and datasets
> fetch the papers API
> and more!

Link below ⬇️

[InSpatio｜影溯 @InSpatio_AI]

We don’t generate videos. 🎬
We generate worlds from videos. 🌍

Introducing InSpatio-World — the world's first open-source real-time 4D world model‼️

Your input: a video clip
Our output: a dynamic, navigable, persistent world

🕹️ explore freely across viewpoints
⏪ control time forward and backward
🔓 open-source and ready to build on :)

Live demo:
🔗 https://t.co/nqU4ZZSqOH

Code & weights:
🔗 https://t.co/rNMilkvKUV

Project page:
🔗 https://t.co/WfMjYGbfzq

[OpenMed @OpenMed_AI]

OpenMed is #1 on Hugging Face's State of Open Source report.

10.4% of all referenced models powering AI research. Ahead of Qwen, Unsloth, NVIDIA, and Meta.

280 models. 50+ RL environments. 16 datasets. All Apache 2.0.

https://t.co/JMtFc4avVg

[Jan Zizka @janz1zka]

Shuttle systems have dominated warehouse automation for 30 years. Today, that era is ending.

I’m excited to announce Brightpick Gridpicker – the fastest robotic fulfillment system ever created.

By combining mobile manipulation with a high-density grid architecture, we’ve created a new paradigm for warehouse automation:
- 2x the throughput vs shuttle systems
- 5x less labor than Goods-to-Person systems
- Cube-level storage density up to 12 meters / 40 ft tall

And all this at up to 40% lower cost than shuttles.

Gridpicker was made possible by two breakthroughs:

1. Mobile manipulation
Each robot completes the entire picking workflow itself instead of splitting tasks between robots and stationary picking stations, boosting productivity. Each robot comes with multiple end-effectors and an automatic tool exchanger to handle a wide range of SKUs.

2. AI
AI is what makes mobile manipulation possible. With 1+ billion picks in production, Brightpick’s AI has a massive data advantage. That means higher pick success rates — and improving performance and cost efficiency over time.

But here’s the real breakthrough:
Gridpicker is the only system whose cost decreases as automation increases.

As more products are picked robotically:
- Each robot becomes more productive
- Fewer robots are required
- Cost per order decreases

Industries get disrupted when new architectures make old trade-offs unnecessary. That’s what Gridpicker does for warehouse automation.

[Black Hole @konstructivizm]

Astronomers have discovered eleven new moons of Saturn.

Most of them are small rocky bodies up to 5 kilometers in diameter. They move in retrograde orbits, meaning they rotate in the opposite direction to Saturn's rotation around its axis.

Until recently, Jupiter was considered the leader in the number of moons. However, in recent years, Saturn has significantly surged ahead. This large number of moons is likely due to a relatively recent (in astronomical terms) collision of large moons. This collision produced numerous fragments, some of which eventually became individual moons. It's possible that this same event played a role in the formation of Saturn's famous rings.

For comparison, 101 moons have been confirmed to date around Jupiter, 29 around Uranus, and 16 around Neptune. This makes Saturn far more numerous than all the other planets in the Solar System combined.

[Dr. Alex Wissner-Gross @alexwg]

https://t.co/J9qU8yakhy

[Sakana AI @SakanaAILabs]

“When AI Discovers the Next Transformer”

Robert Lange (Sakana AI) joins Tim Scarfe (@MLStreetTalk) to discuss Shinka Evolve, a framework that combines LLMs with evolutionary algorithms to do open-ended program search.

Full Video: https://t.co/HWgz62G3Wb

[OpenClaw🦞 @openclaw]

OpenClaw 2026.3.13 🦞

👁️ live Chrome session attach — real logins, one toggle, zero extensions
📱 android redesigned & down to 7MB, iOS gets welcome pager
🐳 docker timezone override
🪟 windows gateway tweaks

the lobster sees all now https://t.co/cPsZ7qOIMw

[Peter Steinberger 🦞 @steipete]

Super excited to see @Microsoft getting involved and helping to make MS Teams top notch for @openclaw!

[Mert Cemri @mertcemri]

AlphaEvolve proved LLMs can discover novel algorithms, but it remains closed-source, and open-source alternatives (OpenEvolve, GEPA) rely on rigid, static search policies.
Introducing AdaEvolve: a fully adaptive evolutionary algorithm that dynamically adjusts its own search strategy based on observed progress.
It matches or beats AlphaEvolve and best known Human SOTA on math and systems benchmarks, and boosts Frontier-CS median scores by 33% over the best open-source baseline across 185 tasks. 🧵👇
(1/n)

[Curious @PUK_BLACK]

AWS与Cerebras合作，通过Amazon Bedrock提供AI推��服务，将推理速度提升一个数量级，使用Trainium3处理计算密集的前填充阶段，Cerebras CS-3处理内存密集的解码阶段。

Cerebras Systems 是一家美国 AI 芯片公司，总部在加州（Sunnyvale），专注给复杂深度学习/大模型提供高性能计算系统。

[Amazon Web Services @awscloud]

We're teaming up with @cerebras to build the fastest possible inference.

Coming soon to Amazon Bedrock, we’re delivering inference performance an order of magnitude faster than what’s available today by connecting AWS Trainium3 for compute-intensive prefill with Cerebras CS-3 to power decode.

Learn more about the partnership. https://t.co/O7piA1GLz1