CryptoPepper

The arrival of Generalist AI's GEN-1—robots are really working
At GTC 2026, two robotic arms autonomously completed phone packaging
NVIDIA is building the "Android" for robots, while Generalist provides the "application layer" agile operation model, and Universal Robots supplies the hardware.
Unlike Figure or Tesla Bot, Generalist focuses solely on models, integrating with other hardware. Light assets, rapid iteration.
2026 will be a watershed year for general-purpose robots. Not because of stunning demos, but because the supply chain has taken shape: chips + models + hardware + scenarios, all

A paper made me stop and read for half an hour. S0 Tuning
Core idea: Without changing the model weights, just tuning an initial state matrix can significantly improve the model's coding ability.
On Qwen3.5-4B, using only 48 HumanEval training samples (not 48K, but 48), S0 tuning increased pass@1 by 23.6 percentage points.
Compared to LoRA, S0 outperformed by 10.8 percentage points. p-value < 0.001, statistically significant.
On FalconH1-7B, S0 achieved 71.8%.
This means that after tuning, the model's speed and size remain unchanged, only the "starting position" is better.
For those

Google releases Gemma 4
Sizes of 1B, 13B, 27B, and a dense 31B version. All under the Apache 2.0 license. Commercial use is unrestricted.
This license change is more significant than the model itself. Previously, Gemma used Google's proprietary license with restrictions. Now, with Apache 2.0, it directly competes with Meta's Llama.
Highlights of the model: multimodal — text + vision + audio. The dense 31B version achieved 89.2% on AIME 2026, LiveCodeBench v6 scored 80%, and it has a Codeforces ELO of 2150.
The 27B parameter size is very friendly for local deployment. It can run on a single 409

OpenAI spent money to buy a YouTube talk show.
TBPN, a tech live broadcast program that only started in 2025, has only 58,000 YouTube followers. OpenAI acquired it.
Last year, TBPN's advertising revenue was $5 million. This year, it is expected to surpass $30 million. In less than two years, the program has grown sixfold.
The show airs live every day at 2 PM for three hours. The guest list includes: Sam Altman, Meta executives, Microsoft executives, Palantir, a16z. Bloomberg and CNBC have also appeared.
This is the living room of Silicon Valley's power circle.
After the acquisition,

Alibaba quietly dropped a big move.
CoPaw-Flash-9B — an AI agent model based on Qwen3.5. With 9 billion parameters, it can run on your own computer.
What's so impressive?
It ties with some benchmarks and Qwen3.5-Plus (closed-source large model).
90 billion parameters vs. hundreds of billions, similar scores.
What excites me even more is the CoPaw framework:
- Supports persistent memory (remembers past conversations)
- Multi-channel connectivity (can connect to Feishu, Discord, etc.)
- Local deployment, no API costs
Qwen3.5's architecture is also very powerful — a total of 397B parameters, with

This market is ultimately destroyed by these kinds of manipulative players messing things up.
One day it surges 18 times, then the battle ends in 15 minutes.
Even with contracts or spot trading on Binance, it's hard to escape the fate.
Retail investors with no info edge will only die faster.
Copycats are finished, and the encryption era is over.

I came across a chart titled "AI Tool Collection," divided into over a dozen categories, and it looks really impressive.
Here's a bold statement — you don't actually need that many tools.
The three I actually use every day are:
- Claude: coding + long-form writing + helping it get to know me
- Codex: messing around with stuff
And occasionally, I add three more: Google Stitch for image creation, Whisper for transcription, Claude Artifact for data analysis.
Five tools. That's enough.
So, what's wrong with that chart? It treats "existence" as "usefulness."
One Claude can replace t

Someone is using Transformers to determine whether loops in code can be parallelized.
Sounds very academic? Don’t worry.
First, the background.
Programmers all know that converting a for loop into a parallel execution is the holy grail of performance optimization. But the problem is: if you get it wrong, bugs happen. Traditional methods rely on static analysis, but they fall apart when faced with complex dependency relationships.
This paper does one thing: it feeds code into a Transformer model (yes, the architecture behind GPT) to let AI judge whether "this loop can be safely parallelized."
W

Spec-heavy, code-light is the correct architectural choice.
What a harness engineer basically means is that your context isn't detailed enough; most people don't even know what they need to plan meticulously.
My hackathon project (which is to hire an AI job-seeking team for you) has been revamped to version 2.0.
I wrote 18 specs, nearly 16 skills, testing every Chinese and English recruitment platform, and only starting to refine after obtaining real experimental data.
Do you see all these posts every day? Have you actually run through the entire process yourself?

RH stock price plummeted 19.5% overnight.
This luxury furniture brand formerly known as Restoration Hardware missed across the board in its earnings report last night.
The numbers are ugly:
Revenue of $843 million, down 3.6% from expectations.
EPS of $1.53, 30.6% below expectations.
Next quarter guidance of $789.5 million, 10.2% below analyst estimates.
The stock dropped directly from $141 to $114.
The company offered two reasons: tariffs causing supply chain reordering, impacting $30 million in revenue.
Bad weather at the end of the quarter also knocked off $10 million.
But

88% of companies have experienced AI agent security incidents. But only 22% treat agents as "identities" to manage them.
Okta CEO Todd McKinnon appeared on The Verge and mentioned something that caught my attention:
AI agents shouldn't just be tools; they should have their own identities. Log in, authenticate, and leave logs just like employees do.
Here's the background.
Currently, more and more AI agents are in enterprises, capable of accessing databases, calling APIs, and sending emails. But most companies still manage agents using the creator's account permissions.
What does this mean? If a

Originally, analyzing the bulls and bears in the market could summarize the U.S. stocks.

Data update:
Morgan Stanley MSBT: 0.14%
Grayscale Mini Trust: 0.15%
Franklin Templeton EZBC: 0.19%
Bitwise / VanEck: 0.20%
BlackRock IBIT: 0.25%
IBIT is currently the absolute leader in the $84 billion BTC spot ETF market. Morgan Stanley is using 0.14% to compete against 0.25%.
Why at this moment?
Three reasons.
First, institutional clients are demanding it. Morgan Stanley manages over $4 trillion in client assets. When your clients are high-net-worth individuals and family offices, they’re not just looking for the best ETF, but for an ETF that’s "approved by compliance." Their own brand + the