GateUser-cc6abff6

Demo for functionality, Production for stability.
Today, a bug in a lower-level SDK's reconnection logic consumed all 65,535 ports in 2 minutes. The system crashed clearly and unmistakably.
This once again validates a fundamental rule of software engineering:
You think you're writing logic, but you're actually writing defenses.
Newcomers obsess over feature implementation; veterans obsess over exception handling:
- Network jitter
- Dependency crashes
- Resource exhaustion
- Invalid input
These are not "accidents"; they are the norm.
A good system is not designed to "avoid" errors, but to "tole

2025 is coming to an end.
My strongest feeling this year is not progress, but the out-of-control density of information.
AI, crypto, storytelling, and opportunities are flooding in simultaneously,
Every day there’s a sense of “If you don’t keep up, you’ll miss everything.”
The more I scroll, the more anxious I become;
The more I learn, the less certain I am about what I’m doing.
Later, I changed direction.
No longer trying to understand the entire world,
But starting to use AI and vibe coding,
Focusing my attention on a specific problem, a real build.
When I began to build instead of just watc

The biggest lesson the "kill zone" has taught me personally is:
I am no longer in a system that allows me to make mistakes freely.
In the past few years, I have been moving towards the "super individual" model:
AI × Crypto × Tools × Content × Personal Brand.
On the surface, it seems like more freedom,
but the real change is—
I have exposed myself to a low tolerance structure.
Many of my current states are actually walking a fine line:
• Income is highly unstable
• Cash flow and energy are tied to output efficiency
• Multiple projects running in parallel, but with thin buffers
• Highly sensitiv

Vibe coding's biggest trap is not that you can't write the code,
but that you suddenly feel like you can do everything.
AI has pushed the "implementation cost" too low,
the result is: every idea seems worth immediately opening a new repo.
But the reality is—
what is truly scarce is never ability, but focus.
Being able to do many things ≠ building something.

I thought LLMs would remember the context, but it turns out they are even more forgetful than a goldfish.
When developing Copilot, I was chatting with the AI through multiple rounds, and finally I couldn't hold back:
It said it had never seen the API documentation I just provided.
It overturned the architecture I just finalized in the next round.
The same bug was fixed three times because it simply didn't remember how I fixed it last time.
That afternoon, I wasted 3 hours repeating "Didn't I just say that?"
Later, I realized: AI is not human; it's a stateless function. If you don't feed it mem

Recently, I keep encountering long articles written by AI, with analyses that are quite insightful, but after reading them, I feel like I haven't gained anything.
The biggest problem isn't actually a lack of information, but rather the flood of synthetic garbage that’s drowning everything.
When AI can produce 10,000 words in a second, 99% of online content naturally depreciates into noise.
For many people, the competitive landscape is shifting: in the past, it was about who could dig up more info; now, it’s more about who can ruthlessly filter out more trash.
We should develop a sufficiently s

Polymarket is not just lively on the surface, with active trading and a surge in users.
The real growth is actually in the third-party ecosystem: PolyAlertHub monitors whale movements in real-time, Polysights uses ML to extract trends, and various copy-trading bots and AI terminals are emerging endlessly.
Not only Polymarket, but platforms like Opinion are also aggressively pushing the builder program to attract developers to create tools.
The core platform is gradually transforming into underlying infrastructure, and the true value is being taken over by the tool layer, lending layer, and var

Recently, I have also been immersed in Vibe Coding.
AI makes coding exceptionally easy, allowing you to piece together a "seemingly complete" system in just a few hours.
That sense of "I can do anything" is very real.
Especially for beginners or those without a technical background,
this external acceleration can easily lead to misjudging your true level of proficiency.
Gradually, I started noticing an abnormal signal:
What truly stalls me is never "being unable to write it,"
but rather, I no longer dare to modify it. 🤦
For tech experts, they might see through architectural issues at a glance

To be honest, I’ve always wanted to find an entry point for combining AI ➕ Crypto, but I never found a way in.
It wasn’t until I came across Vibe Coding that I realized the path was already paved, and taking the first step wasn’t as complicated as I imagined.
You can approach it from several angles, suitable for different groups:
If you’re a solo type: Start from your own needs, create a small tool just for yourself that boosts your efficiency. This is the fastest and most solid way to get started.
If you have a community: You can amplify the tool’s capabilities, turn it into a data dashboard

Now every time I open Xiaohongshu, at least one out of every four posts on the homepage is about Polymarket 😹

Full Messari Report (Extensive Charts):
My invitation link (can slightly reduce some market fees):

In the past week, I have evolved from someone who couldn't write code at all to someone who can build complete systems using AI.
It's not an exaggeration; it's literally "starting from scratch": no knowledge of React, no API, no deployment, no Git, no logic modules, no CSS. Nothing at all.
Now: Polymarket event analysis, world line reasoning, mispricing engine, automatic news scraping, probability calibration, position sizing... I've done it all. (Of course, it's still the basic version that's running, but the core architecture is already in place.)
To put it simply, it

It would be great if the chat history with each large model could be saved, updated, and interconnected.
There is no need to explain the background when switching models, as they are all very familiar with the users' latest status and needs.
Doing coding, creating diagrams, organizing content, or helping oneself solve problems can be done more efficiently.
It can also cross-validate the differences in how different models handle the same problem.

Why is Sports Betting the Biggest Illusion for Newbie Predictors?
When I first came into contact with Polymarket, I thought that the sports market was "easy to understand":
High information density, fast pace, and stimulating market fluctuations seem to provide an edge with a little research.
It wasn't until after a long time that I realized:
The essence of the sports market is one word - randomness.
1) The sports market has no event chain
The charts that I can truly form judgments on are those with clear nodes, traceable signals, deletable paths, and will ultimately converge.
The sports

When we talk to GPT about prediction markets, are we manipulating the model, or is the model reshaping our judgment pathways?

When chatting with GPT or Gemini about predictions:
"Hmm... right... makes sense... steady."
When it's your turn to place a bet:
"Ah? How is it wrong again???"

2/
Why is it important?
Because the Bitcoin mainnet is slow, expensive, and inflexible.
Spark changed everything:
Instant Settlement
The fee is less than 1 cent
Non-custodial operation
Supports stablecoins, instant exchange, lending, NFT
Compatible with Lightning
It transformed BTC from "digital gold" into a "programmable currency layer."