Sergio ‘shadown’ Alvarez

Cybersec Lead @ GE Vernova | 25+ yrs RE/vuln/exploits (embedded/defense). Ex-n.runs/Recurity. AI research since 2017 - (EP3726776A1). Author on AI attacks.

[Sergio ‘shadown’ Alvarez @searchio]

My take on LLM has been keeping track on how things are evolving over the time. This is the latest report: The AI Impact - A Comprehensive Analysis Across All Domains of Society

https://t.co/sw6IOBQpWJ

[Sergio ‘shadown’ Alvarez @searchio]

If other providers down lower their prices they are destined to go bankrupt, period.

[DeepSeek @deepseek_ai]

We are making our discount permanent! 🎉

Enjoy building with DeepSeek-V4-Pro and bring your innovative ideas to life! 🚀

[Sapient Intelligence @Sapient_Int]

In this benchmark deep-dive, Sapient’s founders William and Guan are joined by research team members Changling and Yasin to unpack HRM-Text’s performance across MATH, DROP, ARC-Challenge, and MMLU. 📊

Beyond the scores, they discuss what each benchmark measures, how HRM-Text compares with larger models, and why efficiency matters.

Watch the full discussion to learn more about HRM-Text and Sapient’s leaner path toward general intelligence.

[Sergio ‘shadown’ Alvarez @searchio]

@elonmusk Winning

[Sergio ‘shadown’ Alvarez @searchio]

This article shows how important it is to use several models to analyze and reason during the dataset generation and distillation process. This helps ensure that the dataset used to train the models is as accurate as possible.

https://t.co/ldNzvSoIgN

[Sergio ‘shadown’ Alvarez @searchio]

@awnihannun Congrats! What will happen to MLX?

[Sergio ‘shadown’ Alvarez @searchio]

Yep

[andrew chen @andrewchen]

AI is supposed to save me time, but now I find myself building stuff all evening and weekend and it's actually increasing my time in front of the computer

WTF

[Clara Bennett @CodeswithClara]

🚨BREAKING: Anthropic just dropped free courses to master AI with certificates.

No tuition. No waitlist. No BS.

Here're 10 courses that will replace a $50K degree👇

[Andrej Karpathy @karpathy]

CLIs are super exciting precisely because they are a "legacy" technology, which means AI agents can natively and easily use them, combine them, interact with them via the entire terminal toolkit.

E.g ask your Claude/Codex agent to install this new Polymarket CLI and ask for any arbitrary dashboards or interfaces or logic. The agents will build it for you. Install the Github CLI too and you can ask them to navigate the repo, see issues, PRs, discussions, even the code itself.

Example: Claude built this terminal dashboard in ~3 minutes, of the highest volume polymarkets and the 24hr change. Or you can make it a web app or whatever you want. Even more powerful when you use it as a module of bigger pipelines.

If you have any kind of product or service think: can agents access and use them?

- are your legacy docs (for humans) at least exportable in markdown?
- have you written Skills for your product?
- can your product/service be usable via CLI? Or MCP?
- ...

It's 2026. Build. For. Agents.

[Matthew Berman @MatthewBerman]

I'm one of the most advanced users of OpenClaw.

OpenClaw + GPT5.3 Codex + Opus 4.6 has been the trifecta that changed everything.

I made a video going over everything I'm doing with these tools.

Learn these tools, stay ahead.

Watch this video right now.

0:00 Intro
1:02 Overview
4:17 Sponsor
5:12 Personal CRM
7:11 Knowledge Base
8:30 Video Idea Pipeline
11:09 Twitter/X Search
12:47 Analytics Tracker
13:33 Data Review
15:34 HubSpot
16:13 Humanizer
16:52 Image/Video Generation
18:22 To-Do List
19:37 Usage Tracker (Saves Money)
20:45 Services
21:25 Automations
22:42 Backup
23:30 Memory
24:06 Building OpenClaw
25:22 Updating Files

[Sergio ‘shadown’ Alvarez @searchio]

Clawdbot (now Moltbot) shows how fast a killer AI agent idea can turn into a security mess.

The perfect example of a brilliant idea with zero security foresight.

Lessons learned:

• Agentic AI is game-changing, but secure-by-default is non-negotiable.
• Hype moves fast, security must move faster.

[Rohan Paul @rohanpaul_ai]

New MIT + ETH Zurich + Improbable AI lab paper on scalable, low-overhead continual learning.

Shows Self-Distillation Fine-Tuning improves accuracy from 80% to 89% on knowledge acquisition tasks while reducing catastrophic forgetting.

It uses no extra parameters or reward models, works via in-context prompting,

---

arxiv .org/pdf/2601.19897v1

[Sergio ‘shadown’ Alvarez @searchio]

@LiuGang8964 @callmeMizuko Nice! I didn’t think about different sizes and alignment.

[Sergio ‘shadown’ Alvarez @searchio]

@callmeMizuko 7581 ?

[Rohan Paul @rohanpaul_ai]

Terence Tao predicts the end of math gatekeeping with AI.

AI proof assistants are smashing the technical barriers that keep amateurs out.

By automating verification, AI empowers anyone to contribute rigorous pro-level Math.

The Ivory Tower is falling

[Rohan Paul @rohanpaul_ai]

🇨🇳 China put 256 GW of new solar on the grid in H1-25, while the whole world added 380 GW in that same window, so China alone was 67% of global additions.

For AI's progress, electricity abundance is becoming the absolute key competitive variable

And now 6 months of solar additions in China > decades of solar additions in the US.

[Jonatan Viale @JonatanViale]

Lo importante siempre lo miras en TN
@todonoticias
🙌

[Computer @AskPerplexity]

🚨 BREAKING: DeepSeek just dropped a fundamental improvement in Transformer architecture

CEO Wenfeng Liang on the author list

THE WHALE IS BACK 🐋

[Rohan Paul @rohanpaul_ai]

🚨 BREAKING: China's new opensource code model beats Claude Sonnet 4.5 & GPT 5.1 despite way fewer params.

SWE-Bench Verified (81.4%), BigCodeBench (49.9%), LiveCodeBench v6 (81.1%) - with just 40B-param model.

IQuest-Coder from Quest Research, backed by China’s
quant hedge fund giant UBIQUANT.

UBIQUANT has leaned hard into AI for years, running teams like AILab, DataLab, and Waterdrop Lab.

As of Q3 2025, AUM sat at CNY 70–80B ($10.01–11.43B), with about 24% average returns from Jan to Nov 2025, and CNY 463M ($66.18M) paid out in dividends.

Bifurcated post-training delivers two specialized variants—Thinking models (utilizing reasoning-driven RL for complex problem-solving) and Instruct models (optimized for general coding assistance and instruction-following).

Efficient Architecture: The IQuest-Coder-V1-Loop variant introduces a recurrent mechanism that optimizes the trade-off between model capacity and deployment footprint.

Native Long Context: All models natively support up to 128K tokens without requiring additional scaling techniques.

[Sergio ‘shadown’ Alvarez @searchio]

@JonatanViale La tobillera la sigue teniendo puesta?