GateUser-cc6abff6

Simon Willison, Django core developer and LLM toolkit expert (created tools like LLM CLI, Datasette), mentioned a term in the latest episode of Oxide and Friends podcast called "Deep Blue Moment."
It refers to: when AI surpasses your core skills that you take pride in, leading to a collective psychological crisis.
Originating from the shock in the international chess community after Garry Kasparov was defeated by IBM's Deep Blue in 1997.
Now it's programmers' turn.
Simon said that the amount of hand-written code he produces has dropped to single-digit percentages. The debate over AI writing co

Determine whether what you're doing is worth it:
Ask yourself a question—
"If I stop working for 1 month, will this thing generate value on its own?"
✅ Appreciates (Assets):
- Historical data (accumulates automatically daily)
- Tools used by users (continuous feedback)
- Verified strategies
❌ Does not appreciate (Costs):
- Number of lines of code
- Beautiful architecture diagrams
- 100 unverified TODOs
Compound assets vs. non-assets, decide whether you are "building" or "consuming"

TheSequence's latest issue discusses a trend: many system and agent capabilities are being productized into cutting-edge models.
Functions that previously required complex logic (tool invocation, multi-step reasoning, state management) are now directly built into the models. What does this mean for independent developers and super individuals?
From technical implementation to domain assets as the moat
Pure technical implementation is no longer a barrier. The real moat is: domain knowledge, data accumulation, and product design.
For example, if you develop vertical domain tools, the value isn't

Polymarket's two actions this week are worth watching together:
Starting to charge for 15-minute crypto markets (up to 3%), with all fees fully refunded to market makers
Becoming an exclusive prediction market partner for The Wall Street Journal and Dow Jones
The first move isn't about making money; it's about combating latency arbitrage bots. Short-term crypto markets are most vulnerable to high-frequency bots exploiting them. Using dynamic fee curves to push them out allows genuine market makers to survive. This is a technical aspect of market design.
The second move carries a greater signal

Poly takes a small step, real estate takes a big step.
Polymarket launches a real estate prediction market.
Prediction markets are transforming from tools for financial players into everyday tools, shifting from virtual data pricing to real asset (RWA) pricing.
In the past, people bet on interest rates, inflation, unemployment rates—these are macro data, far from ordinary people.
Now, you can use it to judge "whether to buy a house in San Francisco next year." This is a truly personal decision.
When major life decisions can start to be priced by the market, the information advantage that only

Notion AI 真乃神器也！

Just finished coding on vibe and now I’ve entered the information cocoon of Claude code.

This is a special window period.
AI is already powerful enough, but not fully mature yet. This means that the capability curve of tools and the cognitive gap exist simultaneously.
Most people are still observing "what AI can do," while a few are already using AI to build prediction tools, autonomous trading systems, and closed-loop Agents. These things couldn't be done five years ago, and in five years, they might already be a red ocean.
The opportunity lies within this cognitive gap.
It's not that we can change everything; individuals are ultimately just dust. But participating in shaping thi

Isn't it said that AI can make people do nothing?
Why do I feel more exhausted after having AI?
Ideas keep popping out all day long, 24 hours a day.
Feeling like I can do everything.
Want to try everything😆

Super individuals are really not human, it's too exhausting😂

a16z predicts: The prediction market will enter the "AI Agent Trader" era by 2026
As someone building in this field, I have already felt this trend:
🔍 The number of contracts will explode
→ Not just major elections, but covering all niche events
⚖️ Solving disputes with AI
→ Traditional manual judgments are too slow, LLMs can quickly provide structured reasoning
🤖 AI agents becoming complex traders
→ They can scan signals across the entire network to identify gaps between "market pricing" and "true probabilities"
Prediction markets are not about replacing polls, but about making polls better

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