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OpenAI's usage pattern from CFO Sarah Friar's new interview.
"Our free users do about seven turns, or seven questions, a day. Our first paid tier does double that, about 15. Our real paid tier, Plus, which is $20, is about 3x, and Pro is about 11x over a free user."
Our mission at OpenAI is AGI for the benefit of humanity, not for the benefit of humanity who can pay, or for the benefit of humanity who live in an enterprise"
"Our free users do about seven turns, or seven questions, a day. Our first paid tier does double that, about 15. Our real paid tier, Plus, which is $20, is about 3x, and Pro is about 11x over a free user."
Our mission at OpenAI is AGI for the benefit of humanity, not for the benefit of humanity who can pay, or for the benefit of humanity who live in an enterprise"
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GitHub just sent developers a $138.92 monthly bill.
The tool that was supposed to replace them.
Now it cannot afford to keep itself alive.
Usage-based. No ceiling. No flat rate.
Let that sink in.
The same tool that sold itself as the affordable AI for every developer. The same platform that locked your entire team into its workflow. The same product is losing 39% of its market share.
Just sent you the real invoice.
Here is what affordable actually costs:
→ 10 developers — $1,389 per month
→ 50 developers — $6,946 per month
→ 100 developers — $13,892 per month
→ Every line generated — bill climbs higher
→ Every token consumed — number goes up
They needed your workflow.
They needed your dependency.
They needed the switching cost to feel too high.
You were not the priority. You were the strategy.
The flat rate was real. The loyalty was not.
Teams still on Copilot after June 1st?
They are already behind.
The tool that was supposed to replace them.
Now it cannot afford to keep itself alive.
Usage-based. No ceiling. No flat rate.
Let that sink in.
The same tool that sold itself as the affordable AI for every developer. The same platform that locked your entire team into its workflow. The same product is losing 39% of its market share.
Just sent you the real invoice.
Here is what affordable actually costs:
→ 10 developers — $1,389 per month
→ 50 developers — $6,946 per month
→ 100 developers — $13,892 per month
→ Every line generated — bill climbs higher
→ Every token consumed — number goes up
They needed your workflow.
They needed your dependency.
They needed the switching cost to feel too high.
You were not the priority. You were the strategy.
The flat rate was real. The loyalty was not.
Teams still on Copilot after June 1st?
They are already behind.
✍1
🇨🇭 Global One SI - Prompt
Jane Street made $39.6 billion last year with just 3,500 employees. That’s over $11 million per employee. Almost 7x more than traditional Wall Street firms. They didn’t do it by hiring more analysts. They did it by turning every quant into a machine powered…
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Wednesday, Jun 3 2026, 16:00 UTC time
📋 Global growth & policy pulse
OECD trims near-term forecasts, warns a protracted Iran war could push up inflation and slow growth across Europe and globally. Eurozone PMIs signal May contraction pressures. Central banks are expected to keep tightening in this turmoil.
✅ FX & central bank signals
USD remains resilient as traders await US data; dollar bulls cling to safe-haven bid. Yen slid past 160, drawing official warnings from Japan. Ruble pressures persist—Sberbank says rouble ideally should weaken to ~90/USD to help exporters.
🔖 Commodities & inflation drivers
Oil lifted on renewed Middle East tensions; gold eased on stronger dollar despite geopolitical risk. Silver slipped as inflation fears and rate worries mount. Rising energy costs (Europe, Norway wage talks) and weaker Russian hydrocarbon revenues tighten supply-side inflation risks.
📌 Regional & market-specific movers
- China: PBOC halted reverse repos (first zero since 2015/2024 mentions) — liquidity tap closed, nudging idle cash into economy; signals localized easing of policy operations.
- UK: Lords flag potential BoE regulation of pound stablecoins; regulatory risk for FX/crypto markets.
- US/EU trade: New US forced-labour tariffs hitting 60 economies, EU warns proposed US tariffs on EU goods would be unacceptable; trade tensions could pressure EUR & global trade flows.
- Australia: PMI revisions and slowing economy suggest RBA sidelined for now.
- India: Services PMI revised up; silver import curbs and RBI denying gold sales report—watch INR and metals flows.
- Russia: Oil & gas revenues down ~20.7% month-on-month; economic strain likely to weigh on RUB and fiscal outlook.
- Other: Canada, Germany, France, Italy, Switzerland, Japan, Korea, Brazil & New Zealand — monitor European energy job risks and national PMIs for local FX moves.
Bottom line: Geopolitical war risk + tight energy markets keep inflation and rates elevated, supporting USD strength and commodity-linked currency volatility. Positioning: favor USD safe-haven, hedge commodity exposures, watch central bank signals (PBOC, BoE, Fed, BoJ) for short-term FX pivots.
📋 Global growth & policy pulse
OECD trims near-term forecasts, warns a protracted Iran war could push up inflation and slow growth across Europe and globally. Eurozone PMIs signal May contraction pressures. Central banks are expected to keep tightening in this turmoil.
Implication: slower growth + sticky inflation = volatile rate expectations.✅ FX & central bank signals
USD remains resilient as traders await US data; dollar bulls cling to safe-haven bid. Yen slid past 160, drawing official warnings from Japan. Ruble pressures persist—Sberbank says rouble ideally should weaken to ~90/USD to help exporters.
Implication: risk-off + higher oil/inflation = stronger USD, weaker commodity/exposed currencies.🔖 Commodities & inflation drivers
Oil lifted on renewed Middle East tensions; gold eased on stronger dollar despite geopolitical risk. Silver slipped as inflation fears and rate worries mount. Rising energy costs (Europe, Norway wage talks) and weaker Russian hydrocarbon revenues tighten supply-side inflation risks.
Implication: energy-driven CPI upside supports rates and FX volatility.📌 Regional & market-specific movers
- China: PBOC halted reverse repos (first zero since 2015/2024 mentions) — liquidity tap closed, nudging idle cash into economy; signals localized easing of policy operations.
- UK: Lords flag potential BoE regulation of pound stablecoins; regulatory risk for FX/crypto markets.
- US/EU trade: New US forced-labour tariffs hitting 60 economies, EU warns proposed US tariffs on EU goods would be unacceptable; trade tensions could pressure EUR & global trade flows.
- Australia: PMI revisions and slowing economy suggest RBA sidelined for now.
- India: Services PMI revised up; silver import curbs and RBI denying gold sales report—watch INR and metals flows.
- Russia: Oil & gas revenues down ~20.7% month-on-month; economic strain likely to weigh on RUB and fiscal outlook.
- Other: Canada, Germany, France, Italy, Switzerland, Japan, Korea, Brazil & New Zealand — monitor European energy job risks and national PMIs for local FX moves.
Bottom line: Geopolitical war risk + tight energy markets keep inflation and rates elevated, supporting USD strength and commodity-linked currency volatility. Positioning: favor USD safe-haven, hedge commodity exposures, watch central bank signals (PBOC, BoE, Fed, BoJ) for short-term FX pivots.
🇨🇭 Global One SI - Prompt
6 traders made $470,000+ in profit trading weather markets on Polymarket - a niche most people completely ignore These aren't lucky bets. Each of them built a repeatable edge around temperature data that the market consistently misprices Here's the full…
How to Build a Profitable Trading Bot for Polymarket Weather Markets: Bias Correction Breakdown
If you're seriously building a bot for daily high/low temperature markets on Polymarket, then you need to understand bias correction
I studied the paper "A Machine Learning–Based Bias Correction Method for GFS 2-m Temperature" (Cui et al., Weather and Forecasting, May 2026) and here’s how to do bias correction properly
> What the Paper is About
GFS is one of the most popular models that almost every bot uses. However, it has systematic errors (bias) in 2-meter temperature forecasts.
Especially a cold bias (the model underestimates temperature), which varies depending on time of day, region, and forecast horizon.
> What the Authors Did
• Developed a model called BC-Unet - a U-Net convolutional neural network with CBAM attention block
• Trained it on GFS outputs + ERA5 reanalysis (as ground truth)
• Covered the entire CONUS (USA)
• Trained on one horizon (72 hours) and then applied it to all horizons from 6 hours to 10 days
• Compared results against both ERA5 and real station observations
> Key Results
• Reduced average RMSE down to 0.35°C.
• Reduced cold bias down to 0.8°C (in 00 UTC cycle).
• Excellent performance on diurnal cycle (day/night) - critical for high/low bins.
• Outperforms traditional methods (like MOS).
> How to Calculate Bias
Bias = Forecast − Truth (where Truth = ERA5 or real station observation)
If bias is negative → model systematically underestimates temperature (cold bias)
Bias is calculated separately by:
• Region
• Time of day (day/night - diurnal cycle)
• Forecast horizon
• Season
> How to Reduce Bias
1. Simple Classical Methods (baseline):
• Moving average, Kalman filter, MOS (Model Output Statistics) - linear regression on past errors.
2. ML Approach (BC-Unet):
• The neural network learns nonlinear relationships between raw GFS forecast, orography, time of day, season, and the actual error
• CBAM attention helps the network focus on important areas (mountains, cities, water bodies).
• Train once - apply to all horizons.
• Result: bias is almost eliminated, and the model follows the real diurnal temperature cycle much better
This kind of post-processing (bias correction) turns raw GFS from an average source into a much more accurate one
On Polymarket markets with 1°C steps, this gives a noticeable edge in probability estimation
If you're seriously building a bot for daily high/low temperature markets on Polymarket, then you need to understand bias correction
I studied the paper "A Machine Learning–Based Bias Correction Method for GFS 2-m Temperature" (Cui et al., Weather and Forecasting, May 2026) and here’s how to do bias correction properly
> What the Paper is About
GFS is one of the most popular models that almost every bot uses. However, it has systematic errors (bias) in 2-meter temperature forecasts.
Especially a cold bias (the model underestimates temperature), which varies depending on time of day, region, and forecast horizon.
> What the Authors Did
• Developed a model called BC-Unet - a U-Net convolutional neural network with CBAM attention block
• Trained it on GFS outputs + ERA5 reanalysis (as ground truth)
• Covered the entire CONUS (USA)
• Trained on one horizon (72 hours) and then applied it to all horizons from 6 hours to 10 days
• Compared results against both ERA5 and real station observations
> Key Results
• Reduced average RMSE down to 0.35°C.
• Reduced cold bias down to 0.8°C (in 00 UTC cycle).
• Excellent performance on diurnal cycle (day/night) - critical for high/low bins.
• Outperforms traditional methods (like MOS).
> How to Calculate Bias
Bias = Forecast − Truth (where Truth = ERA5 or real station observation)
If bias is negative → model systematically underestimates temperature (cold bias)
Bias is calculated separately by:
• Region
• Time of day (day/night - diurnal cycle)
• Forecast horizon
• Season
> How to Reduce Bias
1. Simple Classical Methods (baseline):
• Moving average, Kalman filter, MOS (Model Output Statistics) - linear regression on past errors.
2. ML Approach (BC-Unet):
• The neural network learns nonlinear relationships between raw GFS forecast, orography, time of day, season, and the actual error
• CBAM attention helps the network focus on important areas (mountains, cities, water bodies).
• Train once - apply to all horizons.
• Result: bias is almost eliminated, and the model follows the real diurnal temperature cycle much better
This kind of post-processing (bias correction) turns raw GFS from an average source into a much more accurate one
On Polymarket markets with 1°C steps, this gives a noticeable edge in probability estimation
✍1
$JFB Most investors see:
A small construction company.
The market may soon see:
An emerging AI defense platform.
The chart is quietly improving.
- U-bottom developing
- Buyers defending the $5 area
- RSI recovering
- OBV strengthening
- Breakout level near $6.50
If momentum continues building above current levels, the next major zone sits near $8-$10.
And that's before we even discuss the merger catalyst...
Peers: $LPA $AXR $OZ $FOR $NLOP $ARL $STRS $MLP $TCI $MMI
A small construction company.
The market may soon see:
An emerging AI defense platform.
The chart is quietly improving.
- U-bottom developing
- Buyers defending the $5 area
- RSI recovering
- OBV strengthening
- Breakout level near $6.50
If momentum continues building above current levels, the next major zone sits near $8-$10.
And that's before we even discuss the merger catalyst...
Peers: $LPA $AXR $OZ $FOR $NLOP $ARL $STRS $MLP $TCI $MMI
$SMX | Potential Recovery Setup Developing
$SMX has spent months under heavy selling pressure, but the chart is finally starting to show signs of life.
Technical observations:
- Higher lows forming since the mid-May bottom
- Volume steadily improving
- RSI(14) at 29.4 — still deeply oversold
- MFI(14) at 63.2 — momentum improving
- OBV recovering from recent lows
- Trading above key support in the $17–18 area
The key technical trigger now:
➡ A breakout above $20 – 26 could open the door toward $46 – $70+ in a recovery scenario.
For a company with a small float and highly speculative profile, momentum can accelerate very quickly once traders start paying attention.
$SMX has spent months under heavy selling pressure, but the chart is finally starting to show signs of life.
Technical observations:
- Higher lows forming since the mid-May bottom
- Volume steadily improving
- RSI(14) at 29.4 — still deeply oversold
- MFI(14) at 63.2 — momentum improving
- OBV recovering from recent lows
- Trading above key support in the $17–18 area
The key technical trigger now:
➡ A breakout above $20 – 26 could open the door toward $46 – $70+ in a recovery scenario.
For a company with a small float and highly speculative profile, momentum can accelerate very quickly once traders start paying attention.
🇨🇭 Global One SI - Prompt
$SMX | Potential Recovery Setup Developing $SMX has spent months under heavy selling pressure, but the chart is finally starting to show signs of life. Technical observations: - Higher lows forming since the mid-May bottom - Volume steadily improving…
$SMX | The "Age of Parity" Could Be Bigger Than Most Investors Realize
For decades, virgin plastic was simply the cheaper option.
That equation is starting to change.
According to SMX's latest "Age of Parity" thesis, supply chain disruptions, tariffs, resource constraints, carbon regulations, and sustainability requirements are pushing recycled materials closer to cost parity with virgin plastic.
Why does that matter?
Because once recycled materials become economically competitive, verification and traceability become critical.
That's exactly where $SMX is positioning itself.
- Molecular marking technology
- Blockchain-based traceability
- Digital Material Passports
- Circular economy infrastructure
- Supply chain transparency
The market opportunity isn't just recycling.
It's creating trusted, verifiable identities for physical materials moving through global supply chains.
And with only:
~650K shares outstanding
~35K public float
~11.6% short float
It doesn't take much attention for supply and demand dynamics to become very interesting.
High risk?
Absolutely.
But the combination of:
→ sustainability
→ traceability
→ blockchain infrastructure
→ and an exceptionally tight float structure
makes $SMX one of the more unique speculative stories currently trading on Nasdaq.
For decades, virgin plastic was simply the cheaper option.
That equation is starting to change.
According to SMX's latest "Age of Parity" thesis, supply chain disruptions, tariffs, resource constraints, carbon regulations, and sustainability requirements are pushing recycled materials closer to cost parity with virgin plastic.
Why does that matter?
Because once recycled materials become economically competitive, verification and traceability become critical.
That's exactly where $SMX is positioning itself.
- Molecular marking technology
- Blockchain-based traceability
- Digital Material Passports
- Circular economy infrastructure
- Supply chain transparency
The market opportunity isn't just recycling.
It's creating trusted, verifiable identities for physical materials moving through global supply chains.
And with only:
~650K shares outstanding
~35K public float
~11.6% short float
It doesn't take much attention for supply and demand dynamics to become very interesting.
High risk?
Absolutely.
But the combination of:
→ sustainability
→ traceability
→ blockchain infrastructure
→ and an exceptionally tight float structure
makes $SMX one of the more unique speculative stories currently trading on Nasdaq.
$JFB Nice start today.
Volume is starting to build while price remains trapped inside a tight compression pattern.
What stands out:
→ higher lows forming
→ volatility contracting
→ sellers unable to push it lower
→ accumulation continuing under the surface
Most breakouts don't start with excitement.
They start with boredom.
$JFB looks like it's entering that phase now.
The XTEND merger narrative remains one of the more interesting small-cap stories in the market:
- Infrastructure
- AI Robotics
- Defense Technology
A decisive break from this compression could attract a completely different audience.
Watching very closely.
Volume is starting to build while price remains trapped inside a tight compression pattern.
What stands out:
→ higher lows forming
→ volatility contracting
→ sellers unable to push it lower
→ accumulation continuing under the surface
Most breakouts don't start with excitement.
They start with boredom.
$JFB looks like it's entering that phase now.
The XTEND merger narrative remains one of the more interesting small-cap stories in the market:
- Infrastructure
- AI Robotics
- Defense Technology
A decisive break from this compression could attract a completely different audience.
Watching very closely.
🇨🇭 Global One SI - Prompt
Iren Limited $IREN just announced its plans to build a 800MW data center campus in Bundey, Australia 🇦🇺 This is the first Australian data center project announced by Iren
IREN $IREN partners with ElectraNet to secure high voltage grid connections for a new 800MW AI campus in South Australia.
The strategic infrastructure project leverages direct submarine fiber links to connect global markets.
The strategic infrastructure project leverages direct submarine fiber links to connect global markets.
🇨🇭 Global One SI - Prompt
$MU/USD H2 #Indicator #Signal #Trading #GlobalOneAI Globalone.ch #Prompt #AI
Morgan Stanley doubles the price target on Micron $MU to $1,050 while maintaining an Overweight rating.
Analysts predict a severe, industry wide AI memory shortage will persist for at least 2 to 3 years.
Analysts predict a severe, industry wide AI memory shortage will persist for at least 2 to 3 years.
🇨🇭 Global One SI - Prompt
$USAR/USD H2 #Indicator #Signal #Trading #GlobalOneAI Globalone.ch #Prompt #AI
USA Rare Earth $USAR finalizes a massive $1.6B federal funding package under the U.S. CHIPS Program.
The strategic capital injection accelerates the development of a core heavy rare earth mining project in Texas.
The strategic capital injection accelerates the development of a core heavy rare earth mining project in Texas.
🇨🇭 Global One SI - Prompt
$USAR/USD H2 #Indicator #Signal #Trading #GlobalOneAI Globalone.ch #Prompt #AI
Benchmark reiterates a Buy rating on USA Rare Earth $USAR and maintains a $45 price target.
Analysts note that planned capacity expansions are already factored into a model assuming 10,000 tons of annual magnet output by 2030.
Analysts note that planned capacity expansions are already factored into a model assuming 10,000 tons of annual magnet output by 2030.
🇨🇭 Global One SI - Prompt
Algorithmic Trading in Python with Machine Learning Are you looking to enhance your trading strategies with the power of Python and machine learning? Then you need to check out PyBroker! This Python framework is designed for developing algorithmic trading…
Backtesting and feature engineering for quantitative finance (free on GitHub):
github.com/hudson-and-thames/mlfinlab
Here's what's inside:
Backtest Overfitting Tools
Data Structures
Labeling
Sampling
Feature Engineering
Models
Clustering
Cross-Validation
Hyper-Parameter Tuning
Feature Importance
Bet Sizing
Synthetic Data Generation
Networks
Measures of Codependence
Useful Financial Features
github.com/hudson-and-thames/mlfinlab
Here's what's inside:
Backtest Overfitting Tools
Data Structures
Labeling
Sampling
Feature Engineering
Models
Clustering
Cross-Validation
Hyper-Parameter Tuning
Feature Importance
Bet Sizing
Synthetic Data Generation
Networks
Measures of Codependence
Useful Financial Features
✍1
🇨🇭 Global One SI - Prompt
Backtesting and feature engineering for quantitative finance (free on GitHub): github.com/hudson-and-thames/mlfinlab Here's what's inside: Backtest Overfitting Tools Data Structures Labeling Sampling Feature Engineering Models Clustering Cross-Validation…
QuantMind turns every paper, news article, SEC filing, and blog post in quant finance into a queryable knowledge graph.
Hedge funds pay analysts six figures to do this manually.
github.com/LLMQuant/quant-mind
Hedge funds pay analysts six figures to do this manually.
github.com/LLMQuant/quant-mind
✍1
🎞️ DGX Spark Super Gallery at Computex. Product breakdown 👇
Before the RTX Spark there was the DGX Spark. Not to confuse the two, one runs Windows and the other Linux. Here is a breakdown of the GB10 mini-desktops.
1. NVIDIA DGX Spark (Founders Edition)
🖤 NVIDIA Official
💰 $4,699
🧠 GB10 Grace Blackwell Superchip
💾 128GB Unified LPDDR5X
📀 4TB NVMe SSD (self-encrypting)
⚡ 1 PFLOP FP4 AI Performance
Perfect for local 200B parameter models. 🔥
2. MSI DGX Spark
💰 ~$4,599–$4,699
🧠 GB10 Superchip
💾 128GB Unified Memory
📀 4TB NVMe SSD
Compact & premium design with strong cooling. Great for developers. ⚙️
3. ASUS DGX Spark
💰 ~$3,999–$4,699
🧠 GB10 Grace Blackwell
💾 128GB LPDDR5X Unified
📀 4TB Storage
Excellent build quality and connectivity. Popular with creators. 💻
4. Acer Veriton GN100 DGX Spark
💰 ~$4,100
🧠 GB10 Grace Blackwell Superchip
💾 128GB Unified LPDDR5X
📀 4TB NVMe SSD
Strong thermal performance in a compact chassis. Excellent value option. 🟢
5. Gigabyte AI TOP ATOM DGX Spark
💰 ~$4,699
🧠 GB10 Superchip
💾 128GB Unified Memory
📀 4TB NVMe SSD + ConnectX-7 SmartNIC
Often praised for best-in-class performance and cooling. ⚡
6. HP DGX Spark
💰 ~$6,000–$6,599
🧠 GB10 Superchip
💾 128GB Unified Memory
📀 4TB NVMe
Enterprise-grade reliability with HP’s signature design. 🏢
7. Lenovo ThinkStation DGX Spark
💰 ~$6,000
🧠 GB10 Grace Blackwell
💾 128GB Unified RAM
📀 4TB Storage
Built for professional workstations and heavy AI workloads. 💼
8. Dell Pro Max DGX Spark
💰 ~$6,300 (config dependent)
🧠 GB10 Superchip
💾 128GB LPDDR5X
📀 4TB NVMe SSD
Strong enterprise integration and support. Ideal for businesses. 🏭
Before the RTX Spark there was the DGX Spark. Not to confuse the two, one runs Windows and the other Linux. Here is a breakdown of the GB10 mini-desktops.
1. NVIDIA DGX Spark (Founders Edition)
🖤 NVIDIA Official
💰 $4,699
🧠 GB10 Grace Blackwell Superchip
💾 128GB Unified LPDDR5X
📀 4TB NVMe SSD (self-encrypting)
⚡ 1 PFLOP FP4 AI Performance
Perfect for local 200B parameter models. 🔥
2. MSI DGX Spark
💰 ~$4,599–$4,699
🧠 GB10 Superchip
💾 128GB Unified Memory
📀 4TB NVMe SSD
Compact & premium design with strong cooling. Great for developers. ⚙️
3. ASUS DGX Spark
💰 ~$3,999–$4,699
🧠 GB10 Grace Blackwell
💾 128GB LPDDR5X Unified
📀 4TB Storage
Excellent build quality and connectivity. Popular with creators. 💻
4. Acer Veriton GN100 DGX Spark
💰 ~$4,100
🧠 GB10 Grace Blackwell Superchip
💾 128GB Unified LPDDR5X
📀 4TB NVMe SSD
Strong thermal performance in a compact chassis. Excellent value option. 🟢
5. Gigabyte AI TOP ATOM DGX Spark
💰 ~$4,699
🧠 GB10 Superchip
💾 128GB Unified Memory
📀 4TB NVMe SSD + ConnectX-7 SmartNIC
Often praised for best-in-class performance and cooling. ⚡
6. HP DGX Spark
💰 ~$6,000–$6,599
🧠 GB10 Superchip
💾 128GB Unified Memory
📀 4TB NVMe
Enterprise-grade reliability with HP’s signature design. 🏢
7. Lenovo ThinkStation DGX Spark
💰 ~$6,000
🧠 GB10 Grace Blackwell
💾 128GB Unified RAM
📀 4TB Storage
Built for professional workstations and heavy AI workloads. 💼
8. Dell Pro Max DGX Spark
💰 ~$6,300 (config dependent)
🧠 GB10 Superchip
💾 128GB LPDDR5X
📀 4TB NVMe SSD
Strong enterprise integration and support. Ideal for businesses. 🏭
✍1
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WHICHLLM scans your hardware and ranks the best local models from HuggingFace that will actually run on your system.
> Evidence-based ranking from merged benchmarks, not just VRAM size
> Simulate any GPU before buying with a single flag
> Recency-aware scoring so stale leaderboard entries don't mislead you
Stop guessing which model fits your machine.
Github: github.com/Andyyyy64/whichllm
> Evidence-based ranking from merged benchmarks, not just VRAM size
> Simulate any GPU before buying with a single flag
> Recency-aware scoring so stale leaderboard entries don't mislead you
Stop guessing which model fits your machine.
Github: github.com/Andyyyy64/whichllm
✍1