🇨🇭 Global One SI - Prompt
🇨🇳CHINA COULD CRASH THE US STOCK MARKET The weapon isn't tariffs. It's free AI. Open models, near-frontier, up to 33x cheaper already handling more tokens than US models. Solar. EVs. Batteries. Now this. It's called model-dumping.
🚨 CHINA JUST BUILT A CHIP THAT OUTPERFORMS NVIDIA BY UP TO 478 TIMES ON SPECIFIC AI TASKS.
Researchers from Peking University developed a neuromorphic chip that can reconstruct complex brain surfaces in under half a second.
The key difference from a standard GPU is architectural.
Traditional chips like Nvidia's A100 separate memory and processing into two distinct units, creating a bottleneck every time data moves between them.
This chip eliminates that bottleneck by performing storage and computation inside the same memory array simultaneously.
This is important because that bottleneck is one of the main reasons AI training is so expensive and power-hungry today.
Nvidia's dominance is built on solving that problem with brute-force hardware and massive memory bandwidth.
This chip solves it differently, at the architectural level.
The 478x figure applies to a specific task: real time neural surface reconstruction.
It is not a general-purpose performance claim. On most standard AI workloads, Nvidia's H100 and B200 chips still lead by a wide margin.
But the comparison to the A100 matters because the A100 is still the chip most enterprise customers actually run on. Nvidia's newest chips are out of reach for most buyers.
If a 40-nanometer chip built inside China's export-restricted chip environment can outperform the A100 by this margin on targeted workloads, it raises a real question about how necessary cutting-edge chip access actually is for specific AI applications.
This is exactly the argument DeepSeek made in January, that you can match frontier AI performance without frontier hardware, just with better architecture.
That one announcement wiped $600 billion from Nvidia's market cap in a single day.
Researchers from Peking University developed a neuromorphic chip that can reconstruct complex brain surfaces in under half a second.
The key difference from a standard GPU is architectural.
Traditional chips like Nvidia's A100 separate memory and processing into two distinct units, creating a bottleneck every time data moves between them.
This chip eliminates that bottleneck by performing storage and computation inside the same memory array simultaneously.
This is important because that bottleneck is one of the main reasons AI training is so expensive and power-hungry today.
Nvidia's dominance is built on solving that problem with brute-force hardware and massive memory bandwidth.
This chip solves it differently, at the architectural level.
The 478x figure applies to a specific task: real time neural surface reconstruction.
It is not a general-purpose performance claim. On most standard AI workloads, Nvidia's H100 and B200 chips still lead by a wide margin.
But the comparison to the A100 matters because the A100 is still the chip most enterprise customers actually run on. Nvidia's newest chips are out of reach for most buyers.
If a 40-nanometer chip built inside China's export-restricted chip environment can outperform the A100 by this margin on targeted workloads, it raises a real question about how necessary cutting-edge chip access actually is for specific AI applications.
This is exactly the argument DeepSeek made in January, that you can match frontier AI performance without frontier hardware, just with better architecture.
That one announcement wiped $600 billion from Nvidia's market cap in a single day.
🇨🇭 Global One SI - Prompt
Photo
How to build insanely powerful Claude Fable skills that automate your life:
(you'll want to save this)
(you'll want to save this)
✍1
The first AI company that gives us real persistent memory will own the personal assistant market.
Who gets there first?
Who gets there first?
🇨🇭 Global One SI - Prompt
How to build insanely powerful Claude Fable skills that automate your life: (you'll want to save this)
How to prompt Opus 4.8 in Claude Code for maximum power (advice from Anthropic themselves)
✍1
Fable 5 is back and the divide is immediately obvious. People who know how to structure their prompts, use md files for plans and preferences, and know which framework to reach for are getting fantastic results.
People who don't are calling it nerfed.
The model didn't change.
The gap between people who can use it and people who can't just got more visible.
People who don't are calling it nerfed.
The model didn't change.
The gap between people who can use it and people who can't just got more visible.
✍1
Companies are moving from tokenmaxxing to modelmaxxing.
Because AI bills is making unlimited model use harder to defend.
So model routing is becoming massively important now to control AI spending. Cheaper models handle drafts, sorting, summaries, tests, and first-pass reasoning.
Frontier models stay reserved for tasks where failure costs more than the request. Reports show router use rose from about 1% of firms to 5%.
---
Modelmaxxing is simply treating every AI request like a cost-quality decision, not a blank check.
- Some requests can be routed by rules, using user tier, latency, privacy, or budget limits.
- Other requests need task routing, where coding, math, writing, support, or vision work gets matched to specialist models.
- Harder prompts can trigger difficulty routing, sending easy work cheap and serious reasoning upward.
- A cascade setup starts with a smaller model, then escalates only when the answer looks weak.
- Confidence routing goes further by measuring uncertainty before deciding whether to rerun the request.
- Preference routing learns from user ratings, so the system remembers which model usually satisfies people.
- Budget-aware routing controls both model choice and answer length, because long answers also cost money.
- Cache-first routing avoids fresh model calls when similar prompts already produced usable answers.
- Fallback routing protects the product when one provider becomes slow, blocked, overloaded, or expensive.
Because AI bills is making unlimited model use harder to defend.
So model routing is becoming massively important now to control AI spending. Cheaper models handle drafts, sorting, summaries, tests, and first-pass reasoning.
Frontier models stay reserved for tasks where failure costs more than the request. Reports show router use rose from about 1% of firms to 5%.
---
Modelmaxxing is simply treating every AI request like a cost-quality decision, not a blank check.
- Some requests can be routed by rules, using user tier, latency, privacy, or budget limits.
- Other requests need task routing, where coding, math, writing, support, or vision work gets matched to specialist models.
- Harder prompts can trigger difficulty routing, sending easy work cheap and serious reasoning upward.
- A cascade setup starts with a smaller model, then escalates only when the answer looks weak.
- Confidence routing goes further by measuring uncertainty before deciding whether to rerun the request.
- Preference routing learns from user ratings, so the system remembers which model usually satisfies people.
- Budget-aware routing controls both model choice and answer length, because long answers also cost money.
- Cache-first routing avoids fresh model calls when similar prompts already produced usable answers.
- Fallback routing protects the product when one provider becomes slow, blocked, overloaded, or expensive.
🇨🇭 Global One SI - Prompt
$MU/USD H2 #Indicator #Signal #Trading #GlobalOneAI Globalone.ch #Prompt #AI
Micron Technology $MU breaks ground on a $9.3B factory expansion in Hiroshima to ramp up advanced memory chip production.
The major project receives up to $3.1B in direct Japanese government subsidies.
The major project receives up to $3.1B in direct Japanese government subsidies.
Local models are the future
Are they as good as Fable 5? Absolutely not. They're slower and dumber
But having unlimited Opus level intelligence running 24/7 unlocks a TON of new use cases
I have 4 models running on 6 computers.
Around the clock they are:
• Running security scans on my 2 SaaS's codebases
• Optimizing code
• Scraping the internet for business opportunities
• Monitoring all my agents
• Interviewing hypothetical personas for ideas
• Monitoring my database for anomalies
• Monitoring user feedback
• Monitoring competitor's apps and accounts
Doing any of this with a cloud, frontier model would cost thousands a month
But because I have unlimited intelligence running on my desk, I can have these models burning tokens 24/7/365
Yes I still use Fable 5. Yes I still use Codex. That doesn't mean local AI is a waste
Cloud and local complement each other perfectly. It's not one or the other
And with the way things are going, we'll be able to lean completely on local in the next year
Start paying attention to local AI before it's too late
Are they as good as Fable 5? Absolutely not. They're slower and dumber
But having unlimited Opus level intelligence running 24/7 unlocks a TON of new use cases
I have 4 models running on 6 computers.
Around the clock they are:
• Running security scans on my 2 SaaS's codebases
• Optimizing code
• Scraping the internet for business opportunities
• Monitoring all my agents
• Interviewing hypothetical personas for ideas
• Monitoring my database for anomalies
• Monitoring user feedback
• Monitoring competitor's apps and accounts
Doing any of this with a cloud, frontier model would cost thousands a month
But because I have unlimited intelligence running on my desk, I can have these models burning tokens 24/7/365
Yes I still use Fable 5. Yes I still use Codex. That doesn't mean local AI is a waste
Cloud and local complement each other perfectly. It's not one or the other
And with the way things are going, we'll be able to lean completely on local in the next year
Start paying attention to local AI before it's too late
👨💻1
By 2027, just 5 companies, Alphabet, Amazon, Meta, Microsoft, and Oracle, are expected to spend around 3.2% of US GDP on AI capital expenditure.
That would put private AI infrastructure spending above US national defense spending, which is expected to be around 2.7% of GDP.
The AI race is now being funded at a scale normally associated with governments, wars, energy systems, railroads, highways, and telecom buildouts.
The striking part is the speed. AI capex is expected to jump from about 1.5% of GDP in 2025 to about 2.5% in 2026, then to 3.2% in 2027.
AI boom is now large enough to influence the broader US economy significantly, it can move GDP growth, electricity demand, chip supply, construction activity, corporate debt markets, and ofcourse the labor market.
That would put private AI infrastructure spending above US national defense spending, which is expected to be around 2.7% of GDP.
The AI race is now being funded at a scale normally associated with governments, wars, energy systems, railroads, highways, and telecom buildouts.
The striking part is the speed. AI capex is expected to jump from about 1.5% of GDP in 2025 to about 2.5% in 2026, then to 3.2% in 2027.
AI boom is now large enough to influence the broader US economy significantly, it can move GDP growth, electricity demand, chip supply, construction activity, corporate debt markets, and ofcourse the labor market.
🇨🇭 Global One SI - Prompt
If anyone is looking to build a multi-card rig for Local AI, current price ranges for working and new boards on Ebay show price stabilization. Deals can be found. 🔹 RTX-5090 (32GB) $3,599 - $5,199 🔹 RTX-4090 (24GB) $2,099 - $4,499 🔹 RTX-3090 (24GB) $1,100…
My favorite is still this little guy: 2x RTX 5090 with 64 GB of VRAM.
Just the perfect Personal AI Computer 📸🧵
github.com/autonomous-ai/autonomous-computer
Just the perfect Personal AI Computer 📸🧵
github.com/autonomous-ai/autonomous-computer