Theo

[Theo @m_paavaa]

7/ La cosa che mi farebbe cambiare idea?

Un'inflazione di fondo più calda del previsto. In quel caso la scommessa 'tutto rientra' salta, e il premio d'inflazione compresso è il primo a doversi riprezzare.

Tesi falsificabile, sempre.

[Theo @m_paavaa]

6/ Come la leggo io.

Non è il momento di inseguire. È il momento di avere posizioni con un livello di uscita già deciso PRIMA del dato, non dopo.

La disciplina qui vale più della previsione.

[Claude @claudeai]

Fable 5 is back.

[venus @RitOnchain]

https://t.co/fWAlZVWmKU

[Qwen @Alibaba_Qwen]

📣📣 Meet Qwen-AgentWorld — a native language world model that simulates 7 agent environments (MCP, Search, Terminal, SWE, Web, OS, Android) within a single model. Environment modeling is the training objective from day one, not a post-hoc adaptation.

🤔 LLMs are trained to be better agents — better at acting in environments. But nobody has trained them to model the environments themselves.

🗺️ Our roadmap: investigate how language world modeling can push the boundaries of general agent capabilities, along two routes:

1️⃣ Build a foundation model for environment simulation — outperforming Claude Opus 4.8 and GPT-5.4 on AgentWorldBench

2️⃣ Investigate how world modeling enhances agent training:
🔬 Controllable Sim RL (agentic RL with LWM as environments) surpasses training in real environments
🧠 Learning to predict environments (LWM warm-up) makes agents stronger — remarkably, even without any agent-specific training, this predictive knowledge transfers to agentic tasks with zero fine-tuning

📑 Paper: https://t.co/Jx2l5RKq71
📖 Blog: https://t.co/7tVcKyhsx2
💻 GitHub: https://t.co/B5Lvb1UZCn
🤗 HuggingFace: https://t.co/Kw3QBL1TM5
🧩 ModelScope: https://t.co/YBnGYgMWWI

[Sakana AI @SakanaAILabs]

Introducing Sakana Fugu: A full multi-agent orchestration system accessible via a single model API.

Our ‘Fugu Ultra’ model matches the performance of Fable and Mythos, delivering frontier capability without the risk of export controls.

Try it: https://t.co/hhO6qTawgb 🐡

[OpenRouter @OpenRouter]

Introducing the Fusion API, the smartest compound model in the market.

Fusion achieves Fable-level intelligence at half the price.

How it works 👇

[0xDipper @Dipper_pol]

This is the rarest piece of HFT content on the internet - Robert Almgren walking through a real execution trade tick by tick

He's the co-founder of Quantitative Brokers and the man who wrote the Almgren-Chriss model - the standard execution algorithm used at Goldman, Citadel, Two Sigma

In this lecture he shows a live order from May 14 2018, every fill, every limit, every market reaction across 2 minutes and 40 seconds

[zostaff @zostaff]

Jane Street, Goldman Sachs, JP Morgan, BlackRock, Hudson River Trading, Two Sigma, D.E. Shaw.

The most expensive engineering teams in the world released their financial tools on GitHub. Here are 7 repos, one from each.

1. Jane Street, janestreet/magic-trace
https://t.co/a2G20vnewK
5.3k stars. Process tracer powered by Intel PT. When your profiler is blind, magic-trace sees every CPU instruction.

2. Goldman Sachs, goldmansachs/gs-quant
https://t.co/SMYFwP3TWD
Derivative pricing the GS traders use at their desks. MIT licensed.

3. JP Morgan, finos/perspective
https://t.co/9rgy6FxYt4
What JPM traders use to watch markets in real time. A $24k/year terminal, for free.

4. BlackRock, blackrock/lcso
https://t.co/iHwsxZDZD9
Rust optimizer for portfolio problems. Where scipy gives up, this works.

5. Hudson River Trading, hudson-trading/corral
https://t.co/YhmrQFmYaZ
Structured concurrency for C++20. The foundation of HFT infrastructure at one of the largest U.S. trading firms.

6. Two Sigma, twosigma/flint https://t.co/ebEFqcDxJ6
Time-series joins on Apache Spark with temporal tolerance. Built for billions of ticks.

7. D.E. Shaw, deshaw/pyflyby https://t.co/uYDQKtnDVd
Auto-import for IPython and Jupyter. D.E. Shaw also funded the development of IPython itself.

Bookmarked it

[Michael Pyrcz🌻 @GeostatsGuy]

When I taught principal component analysis (#PCA) for #MachineLearning-based #dimensionality reduction a bunch of students asked, 'What do the component loadings look like?'

So, I coded up an interactive #Python @matplotlib
#dashboard! Vary the strength of the correlation and watch the loadings & variance explained change! Super cool!

On #GitHub @ https://t.co/8kuicemoFO∀. #DataScience

[Ziggy @ZigsOnTheBid]

Market makers can't see your stop loss orders, but they and everyone else can see your resting limit orders.

Traders use a depth graph to visualize this data, with different colors representing various order sizes.

In this thread, I'll show you how to set one up.🧵👇

[Smashelito @smashelito]

@trumpspoon The buying aggression after the pullback towards 48s was brutal!

[Documenting ₿itcoin 📄 @DocumentingBTC]

Zoom out

[Mishuman @_Mishuman_]

$500k in funding

Backed by Coinbase

2k supply

Any guesses?

[Mishuman @_Mishuman_]

@TheDegenRadio got the alpha first btw 🔒