🇨🇭 Global One SI - Prompt
How to build insanely powerful agent finance skills with Hermes. Hermes is the best AI agent ever built. And one of its best use cases is for deep financial research. If you inject this prompt into your agent, it builds custom agentic finance skills. You'll…
Dynamic workflows are a cheat code inside Claude Code.
Use them, and your productivity will explode.
Inject this prompt into Claude Code to get started with the new dynamic workflows feature
Use them, and your productivity will explode.
Inject this prompt into Claude Code to get started with the new dynamic workflows feature
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🇨🇭 Global One SI - Prompt
Dynamic workflows are a cheat code inside Claude Code. Use them, and your productivity will explode. Inject this prompt into Claude Code to get started with the new dynamic workflows feature
Hermes agent is the most powerful AI agent ever.
One of its strongest use cases is deep financial research.
Deploy agents to analyze sectors, set price alerts, monitor your portfolio, and more.
Paste this prompt into Hermes to turn it into your personal financial assistant
One of its strongest use cases is deep financial research.
Deploy agents to analyze sectors, set price alerts, monitor your portfolio, and more.
Paste this prompt into Hermes to turn it into your personal financial assistant
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🇨🇭 Global One SI - Prompt
Hermes agent is the most powerful AI agent ever. One of its strongest use cases is deep financial research. Deploy agents to analyze sectors, set price alerts, monitor your portfolio, and more. Paste this prompt into Hermes to turn it into your personal…
Ultimate guide to new Claude dynamic workflows (cheatsheet)
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🇨🇭 Global One SI - Prompt
$QMCO One of the more interesting under-the-radar AI infrastructure charts right now. While everyone is chasing the names that already exploded... $QMCO has quietly spent months repairing its structure. Now: - Higher lows continue forming - Price reclaimed…
$QMCO
One of the most overlooked AI infrastructure stories on the market.
Everyone talks about:
$NVDA
$SMCI
$HPE
$DELL
But AI doesn’t run on compute alone.
It needs:
→ storage
→ data management
→ information infrastructure
That’s where $QMCO operates.
Technically:
- Reclaimed 200MA
- Broke a 1-year downtrend
- Volume exploding
- OBV rising
- TTM Squeeze positive
- Momentum accelerating
The biggest moves often start when a stock transitions from:
“forgotten”
to
“discovered.”
$QMCO is beginning to look like one of those charts.
One of the most overlooked AI infrastructure stories on the market.
Everyone talks about:
$NVDA
$SMCI
$HPE
$DELL
But AI doesn’t run on compute alone.
It needs:
→ storage
→ data management
→ information infrastructure
That’s where $QMCO operates.
Technically:
- Reclaimed 200MA
- Broke a 1-year downtrend
- Volume exploding
- OBV rising
- TTM Squeeze positive
- Momentum accelerating
The biggest moves often start when a stock transitions from:
“forgotten”
to
“discovered.”
$QMCO is beginning to look like one of those charts.
💯1
Local AI hardware = capacity × bandwidth × software stack
- Capacity tells you what fits
- Bandwidth tells you how hard the box can breathe
- The software stack tells you how much of the spec sheet you can actually cash out.
Hardware by Memory Bandwidth
- Mac Studio M3 Ultra: up to 512GB @ 819 GB/s
- RTX PRO 6000 Blackwell: 96GB @ 1792 GB/s
- RTX 5090: 32GB @ 1792 GB/s
- RTX 4090: 24GB @ 1008 GB/s
- RX 7900 XTX: 24GB @ 960 GB/s
- Radeon PRO W7900: 48GB @ 864 GB/s
- AMD Radeon AI PRO R9700: 32GB @ 640 GB/s
- Intel Arc Pro B65: 32GB @ ~608 GB/s
- Tenstorrent Wormhole n300: 24GB @ 576 GB/s
- Tenstorrent Blackhole p150: 32GB @ 512 GB/s + 800G
- MacBook Pro M5 Max: 460-614 GB/s
- MacBook Pro M5 Pro: 307 GB/s
- DGX Spark: 128GB @ 273 GB/s (coherent + CUDA)
- Mac mini M4 Pro: 273 GB/s
- Ryzen AI Max / Strix Halo: ~256 GB/s (~96GB usable GPU)
- MacBook Air M5: 153 GB/s
- Snapdragon X2 Elite: 152-228 GB/s
- Intel Lunar Lake: 136 GB/s
- Snapdragon X Elite: 135 GB/s
- Mac mini M4: 120 GB/s
- Arc Pro B60: 24GB @ ~456 GB/s
Verdict
- GPUs are still the bandwidth kings
- Apple wins: stupid amounts of memory, don’t want to shard across GPUs
- Apple loses: when raw tokens/sec & concurrency matter more
- DGX Spark: coherent memory + NVIDIA stack
- Strix Halo / Ryzen AI Max: first real x86 unified-memory contender
- Tenstorrent: fully OSS stack, excited to see this mature
Fitting ≠ serving
Even if it fits, you still pay for
- bandwidth during decode
- KV cache growth
- dequantization
- batching + concurrency
- scheduler quality
- framework overhead
The only mental model that matters:
1. What must fit?
2. What bandwidth tier do I need?
3. What software stack can actually deliver it?
In short:
- NVIDIA → fastest raw speed
- Apple Studio M3 Ultra → biggest one-box memory
- Strix Halo → first real x86 unified
- DGX Spark → coherent NVIDIA dev appliance
- AMD / Intel Arc → rising alternatives
- Tenstorrent → fully opensource stack
Do ask: “which bottleneck am I buying?”
Not: “which hardware is best?”
- Capacity tells you what fits
- Bandwidth tells you how hard the box can breathe
- The software stack tells you how much of the spec sheet you can actually cash out.
Hardware by Memory Bandwidth
- Mac Studio M3 Ultra: up to 512GB @ 819 GB/s
- RTX PRO 6000 Blackwell: 96GB @ 1792 GB/s
- RTX 5090: 32GB @ 1792 GB/s
- RTX 4090: 24GB @ 1008 GB/s
- RX 7900 XTX: 24GB @ 960 GB/s
- Radeon PRO W7900: 48GB @ 864 GB/s
- AMD Radeon AI PRO R9700: 32GB @ 640 GB/s
- Intel Arc Pro B65: 32GB @ ~608 GB/s
- Tenstorrent Wormhole n300: 24GB @ 576 GB/s
- Tenstorrent Blackhole p150: 32GB @ 512 GB/s + 800G
- MacBook Pro M5 Max: 460-614 GB/s
- MacBook Pro M5 Pro: 307 GB/s
- DGX Spark: 128GB @ 273 GB/s (coherent + CUDA)
- Mac mini M4 Pro: 273 GB/s
- Ryzen AI Max / Strix Halo: ~256 GB/s (~96GB usable GPU)
- MacBook Air M5: 153 GB/s
- Snapdragon X2 Elite: 152-228 GB/s
- Intel Lunar Lake: 136 GB/s
- Snapdragon X Elite: 135 GB/s
- Mac mini M4: 120 GB/s
- Arc Pro B60: 24GB @ ~456 GB/s
Verdict
- GPUs are still the bandwidth kings
- Apple wins: stupid amounts of memory, don’t want to shard across GPUs
- Apple loses: when raw tokens/sec & concurrency matter more
- DGX Spark: coherent memory + NVIDIA stack
- Strix Halo / Ryzen AI Max: first real x86 unified-memory contender
- Tenstorrent: fully OSS stack, excited to see this mature
Fitting ≠ serving
Even if it fits, you still pay for
- bandwidth during decode
- KV cache growth
- dequantization
- batching + concurrency
- scheduler quality
- framework overhead
The only mental model that matters:
1. What must fit?
2. What bandwidth tier do I need?
3. What software stack can actually deliver it?
In short:
- NVIDIA → fastest raw speed
- Apple Studio M3 Ultra → biggest one-box memory
- Strix Halo → first real x86 unified
- DGX Spark → coherent NVIDIA dev appliance
- AMD / Intel Arc → rising alternatives
- Tenstorrent → fully opensource stack
Do ask: “which bottleneck am I buying?”
Not: “which hardware is best?”
✍1
$SUGP Watching this one closely into the open.
🔹 Float under 500K shares
🔹 Borrow fee 132.76%
🔹 7000 shares available to short at huge fees
🔹 Volume expansion pre-market
Recent developments:
✅ Revenue grew 5.6% YoY in FY2025
✅ Secured multiple government & infrastructure projects in Hong Kong
✅ Continued adoption of AI-assisted security screening technology across transportation and cargo screening applications
With a razor-thin float, improving business execution and exposure to AI-enabled security technology, $SUGP is a name on my radar today.
🔹 Float under 500K shares
🔹 Borrow fee 132.76%
🔹 7000 shares available to short at huge fees
🔹 Volume expansion pre-market
Recent developments:
✅ Revenue grew 5.6% YoY in FY2025
✅ Secured multiple government & infrastructure projects in Hong Kong
✅ Continued adoption of AI-assisted security screening technology across transportation and cargo screening applications
With a razor-thin float, improving business execution and exposure to AI-enabled security technology, $SUGP is a name on my radar today.
🇨🇭 Global One SI - Prompt
$SUGP Watching this one closely into the open. 🔹 Float under 500K shares 🔹 Borrow fee 132.76% 🔹 7000 shares available to short at huge fees 🔹 Volume expansion pre-market Recent developments: ✅ Revenue grew 5.6% YoY in FY2025 ✅ Secured multiple government…
$SUGP New contract win worth noting.
The company announced the installation of a next-generation cross-border security and high-speed vehicle clearance system.
This isn't consumer tech.
This is critical infrastructure.
As governments continue investing in smarter border security, transportation screening and automated clearance systems, $SUGP continues expanding its footprint in the sector.
With a sub-500K float, this remains one of the more interesting security technology names on my watchlist today.
The company announced the installation of a next-generation cross-border security and high-speed vehicle clearance system.
This isn't consumer tech.
This is critical infrastructure.
As governments continue investing in smarter border security, transportation screening and automated clearance systems, $SUGP continues expanding its footprint in the sector.
With a sub-500K float, this remains one of the more interesting security technology names on my watchlist today.
🇨🇭 Global One SI - Prompt
Video
3 Simple & Effective Bias Correction Methods for Your Polymarket Weather Trading Bot (Decaying Average + MOS + Kalman Filter)
If you're building a trading bot for weather markets, one of the most common mistakes is jumping straight into U-Net, XGBoost, and other deep learning models
In reality, many top-performing bots start with classical methods. They’re simple, fast to implement, and already remove a large portion of systematic error right from the start
Here are three solid baseline methods you should implement first
1. Decaying Average (Exponential Smoothing)
The most popular and easiest method. You take the model’s past errors and gradually reduce the influence of older data
Formula:
Bias_today = α × Bias_yesterday + (1-α) × Current_error
Usually α is set between 0.95 and 0.98. Excellent at capturing seasonal and regional biases. Almost zero computational cost
2. MOS - Model Output Statistics (Linear Regression)
A meteorology classic that still works extremely well. You train a simple linear model to correct the raw forecast:
Corrected_Temp = a × Raw_Forecast + b + c × Hour + d × Season + ...
You can add cloud cover, wind, land type, etc.
Works very well for fixing the diurnal cycle (day vs night temperatures)
3. Kalman Filter (Adaptive Filter)
The most dynamic of the three. It continuously updates the bias estimate in real time, taking into account how much “trust” the model should give to new observations
Formula:
Bias_new = Bias_old + Kalman_Gain × (Current_error - Bias_old)
The more stable the model, the smaller the Kalman Gain, and vice versa. Adapts excellently to season changes and model updates (GFS/ECMWF)
Why These Methods Are a Must-Have at the Beginning
• They remove most of the systematic bias (often 60-80% of what’s possible)
• Easy to implement and test
• Very low risk of overfitting
Good bias correction doesn’t have to be complicated
These three methods form an excellent foundation on which you can already make consistent money on 1°C bins on Polymarket
If you're building a trading bot for weather markets, one of the most common mistakes is jumping straight into U-Net, XGBoost, and other deep learning models
In reality, many top-performing bots start with classical methods. They’re simple, fast to implement, and already remove a large portion of systematic error right from the start
Here are three solid baseline methods you should implement first
1. Decaying Average (Exponential Smoothing)
The most popular and easiest method. You take the model’s past errors and gradually reduce the influence of older data
Formula:
Bias_today = α × Bias_yesterday + (1-α) × Current_error
Usually α is set between 0.95 and 0.98. Excellent at capturing seasonal and regional biases. Almost zero computational cost
2. MOS - Model Output Statistics (Linear Regression)
A meteorology classic that still works extremely well. You train a simple linear model to correct the raw forecast:
Corrected_Temp = a × Raw_Forecast + b + c × Hour + d × Season + ...
You can add cloud cover, wind, land type, etc.
Works very well for fixing the diurnal cycle (day vs night temperatures)
3. Kalman Filter (Adaptive Filter)
The most dynamic of the three. It continuously updates the bias estimate in real time, taking into account how much “trust” the model should give to new observations
Formula:
Bias_new = Bias_old + Kalman_Gain × (Current_error - Bias_old)
The more stable the model, the smaller the Kalman Gain, and vice versa. Adapts excellently to season changes and model updates (GFS/ECMWF)
Why These Methods Are a Must-Have at the Beginning
• They remove most of the systematic bias (often 60-80% of what’s possible)
• Easy to implement and test
• Very low risk of overfitting
Good bias correction doesn’t have to be complicated
These three methods form an excellent foundation on which you can already make consistent money on 1°C bins on Polymarket
✍1
$AIAI The breakout hasn’t happened yet.
That’s exactly why I’m interested.
Most traders chase after the move.
The best opportunities usually appear during compression.
Current setup:
✅ Tightening triangle
✅ Higher lows
✅ Sellers losing control
✅ TTM Squeeze ON
✅ Breakout level near $13.30
Meanwhile the company continues building at the intersection of:
AI + Blockchain + Digital Infrastructure + Autonomous Systems
A clean break above resistance could quickly bring $15+ back into play.
The chart is coiling.
The narrative is growing.
Now we wait for confirmation. 🚀
#AIAI #AIStocks #Blockchain #SmallCaps #Trading #BreakoutAlert
That’s exactly why I’m interested.
Most traders chase after the move.
The best opportunities usually appear during compression.
Current setup:
✅ Tightening triangle
✅ Higher lows
✅ Sellers losing control
✅ TTM Squeeze ON
✅ Breakout level near $13.30
Meanwhile the company continues building at the intersection of:
AI + Blockchain + Digital Infrastructure + Autonomous Systems
A clean break above resistance could quickly bring $15+ back into play.
The chart is coiling.
The narrative is growing.
Now we wait for confirmation. 🚀
#AIAI #AIStocks #Blockchain #SmallCaps #Trading #BreakoutAlert
🇨🇭 Global One SI - Prompt
$SUGP New contract win worth noting. The company announced the installation of a next-generation cross-border security and high-speed vehicle clearance system. This isn't consumer tech. This is critical infrastructure. As governments continue investing…
$SUGP Update
The story remains the same, but the volume just became impossible to ignore.
🔥 Over 11M shares traded
🔥 Float remains under 500K shares
🔥 Borrow fee recently above 130%
🔥 No shares available to short reported earlier
Technically:
✅ Buyers continue defending the recent lows
✅ Higher-low structure remains intact
✅ OBV has exploded higher, signaling aggressive accumulation
✅ Volume is now multiple times larger than the entire float
The chart is still in recovery mode and needs additional confirmation.
However, this is exactly the type of action traders monitor in micro-float names when a stock begins transitioning from capitulation to accumulation.
On the fundamental side, the company recently announced it was awarded a next-generation cross-border security and high-speed vehicle clearance system installation project, expanding its presence in critical security infrastructure and advanced screening systems.
For now, the key is whether buyers can continue building above the recent base and turn today’s volume into a sustained trend rather than a one-day event.
Definitely one of the more interesting micro-float charts on my screen today.
The story remains the same, but the volume just became impossible to ignore.
🔥 Over 11M shares traded
🔥 Float remains under 500K shares
🔥 Borrow fee recently above 130%
🔥 No shares available to short reported earlier
Technically:
✅ Buyers continue defending the recent lows
✅ Higher-low structure remains intact
✅ OBV has exploded higher, signaling aggressive accumulation
✅ Volume is now multiple times larger than the entire float
The chart is still in recovery mode and needs additional confirmation.
However, this is exactly the type of action traders monitor in micro-float names when a stock begins transitioning from capitulation to accumulation.
On the fundamental side, the company recently announced it was awarded a next-generation cross-border security and high-speed vehicle clearance system installation project, expanding its presence in critical security infrastructure and advanced screening systems.
For now, the key is whether buyers can continue building above the recent base and turn today’s volume into a sustained trend rather than a one-day event.
Definitely one of the more interesting micro-float charts on my screen today.
🔥2