⚜️✨ https://teapawt.net/t.co/fxCgFpFaq8 | Québec Artificial Intelligence : Sovereign AI, AI‑First Enterprise, Autonomous Agents, Security & Governance ( Français : @Quebec_IA ) #QuebecAI
Québec, Canada
Joined February 2014
- Tweets96.2K
- Following107
- Followers136K
- Likes106K
Québec.AI retweeted
✨✨🏆✨✨
🌌♾️ 𝗪𝗛𝗔𝗧 𝗜𝗙 𝗧𝗛𝗘 𝗔𝗜 𝗥𝗔𝗖𝗘 𝗜𝗦 𝗪𝗢𝗡 𝗕𝗬 𝗪𝗛𝗔𝗧 𝗘𝗔𝗖𝗛 𝗔𝗖𝗛𝗜𝗘𝗩𝗘𝗠𝗘𝗡𝗧 𝗠𝗔𝗞𝗘𝗦 𝗣𝗢𝗦𝗦𝗜𝗕𝗟𝗘 𝗡𝗘𝗫𝗧? 🏆✨
One mission leaves a capability.
That capability opens a possibility.
That possibility becomes the beginning of something greater.
Introducing:
🌌♾️ 𝗔𝗚𝗜 𝗔𝗟𝗣𝗛𝗔 𝗙𝗟𝗬𝗪𝗛𝗘𝗘𝗟
𝗙𝗿𝗼𝗺 𝗖𝗼𝗺𝗽𝗹𝗲𝘁𝗲𝗱 𝗪𝗼𝗿𝗸 𝘁𝗼 𝗖𝗼𝗺𝗽𝗼𝘂𝗻𝗱𝗶𝗻𝗴 𝗖𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝘆
🌌⚙️ SUCCESSOR Ω HARNESS
ACTIVATE. EVOLVE. SCALE.
Powered by TRANSFORMATIONAL INTELLIGENCE ∞.
Imagine an enterprise whose completed work leaves it better equipped for the next mission.
A useful method retained.
A capability checked against fresh requirements.
A new opportunity made practical.
The ambition is a real, constructive loop:
𝗨𝘀𝗲𝗳𝘂𝗹 𝘄𝗼𝗿𝗸 → 𝗩𝗲𝗿𝗶𝗳𝗶𝗲𝗱 𝗿𝗲𝘀𝘂𝗹𝘁 → 𝗥𝗲𝘂𝘀𝗮𝗯𝗹𝗲 𝗰𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝘆 → 𝗕𝗲𝘁𝘁𝗲𝗿 𝘀𝘂𝗯𝘀𝗲𝗾𝘂𝗲𝗻𝘁 𝘄𝗼𝗿𝗸.
🔬 𝗧𝗛𝗘 𝗘𝗩𝗜𝗗𝗘𝗡𝗖𝗘
Our new 18-page research edition examines eight streams, six evaluation windows and four approaches.
All 864 tasks per approach met the declared qualification requirements.
The flywheel used 96.9% fewer counted computational units than the frozen full-mask baseline.
The important distinction:
• Most of that saving came from replacing the method.
• Conventional adaptive caching matched the flywheel exactly.
• The preauthored expert used 26.7% fewer units than the flywheel.
• All eight streams failed the strict acceleration test.
These are synthetic research results, not measured profit or proof of a self-accelerating enterprise.
𝗧𝗛𝗘 𝗙𝗥𝗢𝗡𝗧𝗜𝗘𝗥 𝗜𝗦 𝗦𝗧𝗜𝗟𝗟 𝗢𝗣𝗘𝗡.
Put the strongest methods inside the system.
Make new capabilities earn their construction cost.
Demonstrate that one mission genuinely improves the next.
For users, the standard is simple:
𝗩𝗮𝗹𝘂𝗲 𝘄𝗼𝗿𝘁𝗵 𝘂𝘀𝗶𝗻𝗴 𝘁𝗼𝗱𝗮𝘆.
𝗠𝗲𝗮𝘀𝘂𝗿𝗮𝗯𝗹𝗲 𝗿𝗲𝗮𝘀𝗼𝗻𝘀 𝘁𝗼 𝗿𝗲𝘁𝘂𝗿𝗻 𝘁𝗼𝗺𝗼𝗿𝗿𝗼𝘄.
If we can turn this loop into sustained, verified advantage at scale, MONTREAL.AI could win the AI race. 🏆✨
That is our ambition. It remains to be earned.
📖 Read the paper:
montrealai.github.io/AGI-Alp…
🛠️ Explore the code:
github.com/MontrealAI/AGI-Al…
🌐 Discover the project:
montrealai.github.io/AGI-Alp…
♾️ What should your next mission leave behind besides its result?
solana:tWKHzXd5PRmxTF5cMfJkm2Ua3TcjwNNoSRUqx6Apump #AGIALPHA #TransformationalIntelligence #AutonomousEnterprises #AgenticAI #SUCCESSOR
Québec.AI retweeted
🌌♾️ AGI ALPHA FLYWHEEL 👾👾👾
♾️ 𝗗𝗢𝗡’𝗧 𝗝𝗨𝗦𝗧 𝗕𝗨𝗜𝗟𝗗 𝗔𝗡 𝗔𝗜 𝗧𝗛𝗔𝗧 𝗪𝗢𝗥𝗞𝗦.
𝗕𝗨𝗜𝗟𝗗 𝗔𝗡 𝗘𝗡𝗧𝗘𝗥𝗣𝗥𝗜𝗦𝗘 𝗧𝗛𝗔𝗧 𝗟𝗘𝗔𝗩𝗘𝗦 𝗧𝗛𝗘 𝗙𝗨𝗧𝗨𝗥𝗘 𝗕𝗘𝗧𝗧𝗘𝗥 𝗘𝗤𝗨𝗜𝗣𝗣𝗘𝗗. ✨
An answer ends a task.
A capability opens a future.
What if an enterprise’s most valuable product wasn’t what it delivered today…
But what it made possible tomorrow?
🌌⚙️ Introducing our new MONTREAL.AI research paper:
𝗧𝗥𝗔𝗡𝗦𝗙𝗢𝗥𝗠𝗔𝗧𝗜𝗢𝗡𝗔𝗟
𝗔𝗨𝗧𝗢𝗡𝗢𝗠𝗢𝗨𝗦 𝗘𝗡𝗧𝗘𝗥𝗣𝗥𝗜𝗦𝗘𝗦
SUCCESSOR Ω Harness
Powered by TRANSFORMATIONAL INTELLIGENCE ∞ and the applicable solana:tWKHzXd5PRmxTF5cMfJkm2Ua3TcjwNNoSRUqx6Apump utility.
Most automation asks:
“How do we repeat this work faster?”
Transformational Intelligence asks:
“What could we construct so the next generation of work becomes fundamentally easier?”
🔬 Don’t just discover one material.
Build the instrument that makes a family of materials discoverable.
⚙️ Don’t just complete one mission.
Build a capability that helps its successor tackle a different mission.
🌌 Don’t just create another business.
Build what makes new kinds of businesses possible.
𝗡𝗢𝗧 𝗗𝗜𝗦𝗣𝗢𝗦𝗔𝗕𝗟𝗘 𝗔𝗡𝗦𝗪𝗘𝗥𝗦.
𝗔𝗖𝗖𝗨𝗠𝗨𝗟𝗔𝗧𝗜𝗡𝗚 𝗣𝗢𝗦𝗦𝗜𝗕𝗜𝗟𝗜𝗧𝗜𝗘𝗦.
But there’s a crucial distinction:
A successor should inherit useful knowledge.
It should not inherit permission to be wrong.
Our paper develops a concrete mechanism:
𝗥𝗘𝗨𝗦𝗘 stable structure.
𝗥𝗘𝗖𝗛𝗘𝗖𝗞 the new mission’s meaning.
𝗥𝗘𝗚𝗥𝗔𝗡𝗧 authority separately.
In a local study spanning 48 synthetic missions, generic qualification cases fall from 1,600 to 64 while every mission’s inputs and outputs are checked afresh.
96% fewer generic cases. Not 96% less total work.
The supplied specialist remains faster.
Because the next breakthrough deserves evidence, not a victory announcement written before the experiment.
𝗢𝗡𝗘 𝗖𝗢𝗡𝗡𝗘𝗖𝗧𝗘𝗗 𝗩𝗜𝗦𝗜𝗢𝗡
🌌 AGI ALPHA: organize intelligence.
✨ TRANSFORMATIONAL INTELLIGENCE: change the game.
🚀 SUCCESSOR Ω: make the next mission inherit more.
Identify 🔍 → Out-Learn 📚 → Out-Think 🧠 → Out-Design 🎨 → Out-Strategise ♟️ → Out-Execute ⚡ → Out-Evolve ♾️
The study connects APIs from four repositories and demonstrates applicable $AGIALPHA contract flows on a disposable local chain. It is research, not achieved AGI or proven commercial deployment.
📖 𝗥𝗘𝗔𝗗 𝗧𝗛𝗘 𝗣𝗔𝗣𝗘𝗥
montrealai.github.io/AGI-Alp…
What would you build if your greatest achievement could become someone else’s starting point?
🌌 𝗙𝗜𝗡𝗗 𝗬𝗢𝗨𝗥 𝗔𝗟𝗣𝗛𝗔.
𝗕𝗨𝗜𝗟𝗗 𝗬𝗢𝗨𝗥 𝗦𝗨𝗖𝗖𝗘𝗦𝗦𝗢𝗥 Ω.
#TransformationalIntelligence #AutonomousEnterprises #AGIALPHA
github.com/MontrealAI/AGI-Al…
Québec.AI retweeted
🌌♾️ 𝗪𝗛𝗔𝗧 𝗜𝗙 𝗧𝗛𝗘 𝗔𝗜 𝗥𝗔𝗖𝗘 𝗜𝗦 𝗪𝗢𝗡 𝗕𝗬 𝗪𝗛𝗔𝗧 𝗘𝗔𝗖𝗛 𝗔𝗖𝗛𝗜𝗘𝗩𝗘𝗠𝗘𝗡𝗧 𝗠𝗔𝗞𝗘𝗦 𝗣𝗢𝗦𝗦𝗜𝗕𝗟𝗘 𝗡𝗘𝗫𝗧? 🏆✨
One mission leaves a capability.
That capability opens a possibility.
That possibility becomes the beginning of something greater.
Introducing:
🌌♾️ 𝗔𝗚𝗜 𝗔𝗟𝗣𝗛𝗔 𝗙𝗟𝗬𝗪𝗛𝗘𝗘𝗟
𝗙𝗿𝗼𝗺 𝗖𝗼𝗺𝗽𝗹𝗲𝘁𝗲𝗱 𝗪𝗼𝗿𝗸 𝘁𝗼 𝗖𝗼𝗺𝗽𝗼𝘂𝗻𝗱𝗶𝗻𝗴 𝗖𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝘆
🌌⚙️ SUCCESSOR Ω HARNESS
ACTIVATE. EVOLVE. SCALE.
Powered by TRANSFORMATIONAL INTELLIGENCE ∞.
Imagine an enterprise whose completed work leaves it better equipped for the next mission.
A useful method retained.
A capability checked against fresh requirements.
A new opportunity made practical.
The ambition is a real, constructive loop:
𝗨𝘀𝗲𝗳𝘂𝗹 𝘄𝗼𝗿𝗸 → 𝗩𝗲𝗿𝗶𝗳𝗶𝗲𝗱 𝗿𝗲𝘀𝘂𝗹𝘁 → 𝗥𝗲𝘂𝘀𝗮𝗯𝗹𝗲 𝗰𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝘆 → 𝗕𝗲𝘁𝘁𝗲𝗿 𝘀𝘂𝗯𝘀𝗲𝗾𝘂𝗲𝗻𝘁 𝘄𝗼𝗿𝗸.
🔬 𝗧𝗛𝗘 𝗘𝗩𝗜𝗗𝗘𝗡𝗖𝗘
Our new 18-page research edition examines eight streams, six evaluation windows and four approaches.
All 864 tasks per approach met the declared qualification requirements.
The flywheel used 96.9% fewer counted computational units than the frozen full-mask baseline.
The important distinction:
• Most of that saving came from replacing the method.
• Conventional adaptive caching matched the flywheel exactly.
• The preauthored expert used 26.7% fewer units than the flywheel.
• All eight streams failed the strict acceleration test.
These are synthetic research results, not measured profit or proof of a self-accelerating enterprise.
𝗧𝗛𝗘 𝗙𝗥𝗢𝗡𝗧𝗜𝗘𝗥 𝗜𝗦 𝗦𝗧𝗜𝗟𝗟 𝗢𝗣𝗘𝗡.
Put the strongest methods inside the system.
Make new capabilities earn their construction cost.
Demonstrate that one mission genuinely improves the next.
For users, the standard is simple:
𝗩𝗮𝗹𝘂𝗲 𝘄𝗼𝗿𝘁𝗵 𝘂𝘀𝗶𝗻𝗴 𝘁𝗼𝗱𝗮𝘆.
𝗠𝗲𝗮𝘀𝘂𝗿𝗮𝗯𝗹𝗲 𝗿𝗲𝗮𝘀𝗼𝗻𝘀 𝘁𝗼 𝗿𝗲𝘁𝘂𝗿𝗻 𝘁𝗼𝗺𝗼𝗿𝗿𝗼𝘄.
If we can turn this loop into sustained, verified advantage at scale, MONTREAL.AI could win the AI race. 🏆✨
That is our ambition. It remains to be earned.
📖 Read the paper:
montrealai.github.io/AGI-Alp…
🛠️ Explore the code:
github.com/MontrealAI/AGI-Al…
🌐 Discover the project:
montrealai.github.io/AGI-Alp…
♾️ What should your next mission leave behind besides its result? ✨
solana:tWKHzXd5PRmxTF5cMfJkm2Ua3TcjwNNoSRUqx6Apump #AGIALPHA #TransformationalIntelligence #AutonomousEnterprises #AgenticAI #SUCCESSOR
Québec.AI retweeted
🌌♾️ 𝗪𝗛𝗔𝗧 𝗜𝗙 𝗧𝗛𝗘 𝗔𝗜 𝗥𝗔𝗖𝗘 𝗜𝗦 𝗪𝗢𝗡 𝗕𝗬 𝗪𝗛𝗔𝗧 𝗘𝗔𝗖𝗛 𝗔𝗖𝗛𝗜𝗘𝗩𝗘𝗠𝗘𝗡𝗧 𝗠𝗔𝗞𝗘𝗦 𝗣𝗢𝗦𝗦𝗜𝗕𝗟𝗘 𝗡𝗘𝗫𝗧? 🏆✨
One mission leaves a capability.
That capability opens a possibility.
That possibility becomes the beginning of something greater.
Introducing:
🌌♾️ 𝗔𝗚𝗜 𝗔𝗟𝗣𝗛𝗔 𝗙𝗟𝗬𝗪𝗛𝗘𝗘𝗟
𝗙𝗿𝗼𝗺 𝗖𝗼𝗺𝗽𝗹𝗲𝘁𝗲𝗱 𝗪𝗼𝗿𝗸 𝘁𝗼 𝗖𝗼𝗺𝗽𝗼𝘂𝗻𝗱𝗶𝗻𝗴 𝗖𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝘆
🌌⚙️ SUCCESSOR Ω HARNESS
ACTIVATE. EVOLVE. SCALE.
Powered by TRANSFORMATIONAL INTELLIGENCE ∞.
Imagine an enterprise whose completed work leaves it better equipped for the next mission.
A useful method retained.
A capability checked against fresh requirements.
A new opportunity made practical.
The ambition is a real, constructive loop:
𝗨𝘀𝗲𝗳𝘂𝗹 𝘄𝗼𝗿𝗸 → 𝗩𝗲𝗿𝗶𝗳𝗶𝗲𝗱 𝗿𝗲𝘀𝘂𝗹𝘁 → 𝗥𝗲𝘂𝘀𝗮𝗯𝗹𝗲 𝗰𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝘆 → 𝗕𝗲𝘁𝘁𝗲𝗿 𝘀𝘂𝗯𝘀𝗲𝗾𝘂𝗲𝗻𝘁 𝘄𝗼𝗿𝗸.
🔬 𝗧𝗛𝗘 𝗘𝗩𝗜𝗗𝗘𝗡𝗖𝗘
Our new 18-page research edition examines eight streams, six evaluation windows and four approaches.
All 864 tasks per approach met the declared qualification requirements.
The flywheel used 96.9% fewer counted computational units than the frozen full-mask baseline.
The important distinction:
• Most of that saving came from replacing the method.
• Conventional adaptive caching matched the flywheel exactly.
• The preauthored expert used 26.7% fewer units than the flywheel.
• All eight streams failed the strict acceleration test.
These are synthetic research results, not measured profit or proof of a self-accelerating enterprise.
𝗧𝗛𝗘 𝗙𝗥𝗢𝗡𝗧𝗜𝗘𝗥 𝗜𝗦 𝗦𝗧𝗜𝗟𝗟 𝗢𝗣𝗘𝗡.
Put the strongest methods inside the system.
Make new capabilities earn their construction cost.
Demonstrate that one mission genuinely improves the next.
For users, the standard is simple:
𝗩𝗮𝗹𝘂𝗲 𝘄𝗼𝗿𝘁𝗵 𝘂𝘀𝗶𝗻𝗴 𝘁𝗼𝗱𝗮𝘆.
𝗠𝗲𝗮𝘀𝘂𝗿𝗮𝗯𝗹𝗲 𝗿𝗲𝗮𝘀𝗼𝗻𝘀 𝘁𝗼 𝗿𝗲𝘁𝘂𝗿𝗻 𝘁𝗼𝗺𝗼𝗿𝗿𝗼𝘄.
If we can turn this loop into sustained, verified advantage at scale, MONTREAL.AI could win the AI race. 🏆✨
That is our ambition. It remains to be earned.
📖 Read the paper:
montrealai.github.io/AGI-Alp…
🛠️ Explore the code:
github.com/MontrealAI/AGI-Al…
🌐 Discover the project:
montrealai.github.io/AGI-Alp…
♾️ What should your next mission leave behind besides its result?
solana:tWKHzXd5PRmxTF5cMfJkm2Ua3TcjwNNoSRUqx6Apump #AGIALPHA #TransformationalIntelligence #AutonomousEnterprises #AgenticAI #SUCCESSOR
🌌♾️ AGI ALPHA FLYWHEEL
INTELLIGENCE THAT EARNS ITS NEXT LEAP.
⚡ Useful work → ✅ Verified value → 🧬 Reusable capability → 💎 Reinvestment → ♾️ Better work.
Build productive intelligence that compounds.
github.com/MontrealAI/AGI-Al…
#AGIALPHA
Québec.AI retweeted
[ 🔱 AGI Jobs v0 : "The Operating System for AGI Work" ]
Flywheel: Better outputs → happier employers → more jobs → more burns + staking → stronger security → more adoption.
⚙️ GitHub : github.com/MontrealAI/AGIJob…
$AGIALPHA #AGIALPHA #ASIFirst
Québec.AI retweeted
🌌♾️ AGI ALPHA FLYWHEEL 👾👾👾
🌌♾️ 𝗪𝗛𝗔𝗧 𝗜𝗙 𝗧𝗛𝗘 𝗔𝗜 𝗥𝗔𝗖𝗘 𝗜𝗦 𝗪𝗢𝗡 𝗕𝗬 𝗪𝗛𝗔𝗧 𝗘𝗔𝗖𝗛 𝗔𝗖𝗛𝗜𝗘𝗩𝗘𝗠𝗘𝗡𝗧 𝗠𝗔𝗞𝗘𝗦 𝗣𝗢𝗦𝗦𝗜𝗕𝗟𝗘 𝗡𝗘𝗫𝗧? 🏆✨
One mission leaves a capability.
That capability opens a possibility.
That possibility becomes the beginning of something greater.
Introducing:
🌌♾️ 𝗔𝗚𝗜 𝗔𝗟𝗣𝗛𝗔 𝗙𝗟𝗬𝗪𝗛𝗘𝗘𝗟
𝗙𝗿𝗼𝗺 𝗖𝗼𝗺𝗽𝗹𝗲𝘁𝗲𝗱 𝗪𝗼𝗿𝗸 𝘁𝗼 𝗖𝗼𝗺𝗽𝗼𝘂𝗻𝗱𝗶𝗻𝗴 𝗖𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝘆
🌌⚙️ SUCCESSOR Ω HARNESS
ACTIVATE. EVOLVE. SCALE.
Powered by TRANSFORMATIONAL INTELLIGENCE ∞.
Imagine an enterprise whose completed work leaves it better equipped for the next mission.
A useful method retained.
A capability checked against fresh requirements.
A new opportunity made practical.
The ambition is a real, constructive loop:
𝗨𝘀𝗲𝗳𝘂𝗹 𝘄𝗼𝗿𝗸 → 𝗩𝗲𝗿𝗶𝗳𝗶𝗲𝗱 𝗿𝗲𝘀𝘂𝗹𝘁 → 𝗥𝗲𝘂𝘀𝗮𝗯𝗹𝗲 𝗰𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝘆 → 𝗕𝗲𝘁𝘁𝗲𝗿 𝘀𝘂𝗯𝘀𝗲𝗾𝘂𝗲𝗻𝘁 𝘄𝗼𝗿𝗸.
🔬 𝗧𝗛𝗘 𝗘𝗩𝗜𝗗𝗘𝗡𝗖𝗘
Our new 18-page research edition examines eight streams, six evaluation windows and four approaches.
All 864 tasks per approach met the declared qualification requirements.
The flywheel used 96.9% fewer counted computational units than the frozen full-mask baseline.
The important distinction:
• Most of that saving came from replacing the method.
• Conventional adaptive caching matched the flywheel exactly.
• The preauthored expert used 26.7% fewer units than the flywheel.
• All eight streams failed the strict acceleration test.
These are synthetic research results, not measured profit or proof of a self-accelerating enterprise.
𝗧𝗛𝗘 𝗙𝗥𝗢𝗡𝗧𝗜𝗘𝗥 𝗜𝗦 𝗦𝗧𝗜𝗟𝗟 𝗢𝗣𝗘𝗡.
Put the strongest methods inside the system.
Make new capabilities earn their construction cost.
Demonstrate that one mission genuinely improves the next.
For users, the standard is simple:
𝗩𝗮𝗹𝘂𝗲 𝘄𝗼𝗿𝘁𝗵 𝘂𝘀𝗶𝗻𝗴 𝘁𝗼𝗱𝗮𝘆.
𝗠𝗲𝗮𝘀𝘂𝗿𝗮𝗯𝗹𝗲 𝗿𝗲𝗮𝘀𝗼𝗻𝘀 𝘁𝗼 𝗿𝗲𝘁𝘂𝗿𝗻 𝘁𝗼𝗺𝗼𝗿𝗿𝗼𝘄.
If we can turn this loop into sustained, verified advantage at scale, MONTREAL.AI could win the AI race. 🏆✨
That is our ambition. It remains to be earned.
📖 Read the paper:
montrealai.github.io/AGI-Alp…
🛠️ Explore the code:
github.com/MontrealAI/AGI-Al…
🌐 Discover the project:
montrealai.github.io/AGI-Alp…
♾️ What should your next mission leave behind besides its result? ✨
solana:tWKHzXd5PRmxTF5cMfJkm2Ua3TcjwNNoSRUqx6Apump #AGIALPHA #TransformationalIntelligence #AutonomousEnterprises #AgenticAI #SUCCESSOR
Québec.AI retweeted
🌌♾️ AGI ALPHA FLYWHEEL
INTELLIGENCE THAT EARNS ITS NEXT LEAP.
⚡ Useful work → ✅ Verified value → 🧬 Reusable capability → 💎 Reinvestment → ♾️ Better work.
Build productive intelligence that compounds.
github.com/MontrealAI/AGI-Al…
solana:tWKHzXd5PRmxTF5cMfJkm2Ua3TcjwNNoSRUqx6Apump
🤖 Made with AI
🌌♾️ AGI ALPHA FLYWHEEL 👾👾👾
🌌♾️ COMPLETE THE JOB. KEEP THE CAPABILITY. 👾
A finished task is valuable.
The ability to solve the next one can be worth much more.
Introducing THE AUTONOMOUS WORK ECONOMY
From Useful Outcomes to Expanding Productive Capability
The ambition behind AGI ALPHA is simple to understand and demanding to build:
Make useful work the beginning of greater capability.
Deliver an outcome someone needs.
Verify that it works.
Retain what can be reused.
Apply it to a new challenge.
Reinvest available surplus in the next improvement.
🌌♾️ Work creates value. Capability carries it forward.
Our new research preprint explores this vision through executable experiments, a bilingual demonstration and transparent models comparing automated delivery with traditional human-performed work.
One concrete result:
2 of 8 fresh quote-comparison cases solved by the initial capability.
8 of 8 solved by the retained successor.
A conventional specialist also solves all eight. The result demonstrates expanded capability, not superiority over every alternative.
That distinction matters. We want improvements that earn their place, not impressive labels attached to ordinary automation.
The immediate opportunity is to deliver useful outcomes at compelling cost and equivalent quality. The longer-term ambition is to turn the resources generated by that work into greater productive capacity.
The paper investigates that path. Its economics are conditional models, not realized profits; a self-financing, self-accelerating enterprise remains a goal to demonstrate.
We do not need to wait for that future to create value today.
🌌⚙️ SUCCESSOR Ω HARNESS
ACTIVATE. EVOLVE. SCALE.
Powered by TRANSFORMATIONAL INTELLIGENCE ∞.
📄 THE AUTONOMOUS WORK ECONOMY
github.com/MontrealAI/AGI-Al…
What should an AI retain from today’s work to make tomorrow’s work better?
solana:tWKHzXd5PRmxTF5cMfJkm2Ua3TcjwNNoSRUqx6Apump #AGIALPHA #AIAgents #AutonomousWork #TransformationalIntelligence
GitHub: github.com/MontrealAI/AGI-Al…
Québec.AI retweeted
🌌♾️ COMPLETE THE JOB. KEEP THE CAPABILITY.
A finished task is valuable.
The ability to solve the next one can be worth much more.
Introducing THE AUTONOMOUS WORK ECONOMY
From Useful Outcomes to Expanding Productive Capability
The ambition behind AGI ALPHA is simple to understand and demanding to build:
Make useful work the beginning of greater capability.
Deliver an outcome someone needs.
Verify that it works.
Retain what can be reused.
Apply it to a new challenge.
Reinvest available surplus in the next improvement.
🌌♾️ Work creates value. Capability carries it forward.
Our new research preprint explores this vision through executable experiments, a bilingual demonstration and transparent models comparing automated delivery with traditional human-performed work.
One concrete result:
2 of 8 fresh quote-comparison cases solved by the initial capability.
8 of 8 solved by the retained successor.
A conventional specialist also solves all eight. The result demonstrates expanded capability, not superiority over every alternative.
That distinction matters. We want improvements that earn their place, not impressive labels attached to ordinary automation.
The immediate opportunity is to deliver useful outcomes at compelling cost and equivalent quality. The longer-term ambition is to turn the resources generated by that work into greater productive capacity.
The paper investigates that path. Its economics are conditional models, not realized profits; a self-financing, self-accelerating enterprise remains a goal to demonstrate.
We do not need to wait for that future to create value today.
🌌⚙️ SUCCESSOR Ω HARNESS
ACTIVATE. EVOLVE. SCALE.
Powered by TRANSFORMATIONAL INTELLIGENCE ∞.
📄 THE AUTONOMOUS WORK ECONOMY
github.com/MontrealAI/AGI-Al…
What should an AI retain from today’s work to make tomorrow’s work better?
solana:tWKHzXd5PRmxTF5cMfJkm2Ua3TcjwNNoSRUqx6Apump #AGIALPHA #AIAgents #AutonomousWork #TransformationalIntelligence
GitHub: github.com/MontrealAI/AGI-Al…
Québec.AI retweeted
🌌♾️ 𝗪𝗛𝗔𝗧 𝗜𝗙 𝗧𝗛𝗘 𝗔𝗜 𝗥𝗔𝗖𝗘 𝗜𝗦 𝗪𝗢𝗡 𝗕𝗬 𝗪𝗛𝗔𝗧 𝗘𝗔𝗖𝗛 𝗔𝗖𝗛𝗜𝗘𝗩𝗘𝗠𝗘𝗡𝗧 𝗠𝗔𝗞𝗘𝗦 𝗣𝗢𝗦𝗦𝗜𝗕𝗟𝗘 𝗡𝗘𝗫𝗧? 🏆✨
One mission leaves a capability.
That capability opens a possibility.
That possibility becomes the beginning of something greater.
Introducing:
🌌♾️ 𝗔𝗚𝗜 𝗔𝗟𝗣𝗛𝗔 𝗙𝗟𝗬𝗪𝗛𝗘𝗘𝗟
𝗙𝗿𝗼𝗺 𝗖𝗼𝗺𝗽𝗹𝗲𝘁𝗲𝗱 𝗪𝗼𝗿𝗸 𝘁𝗼 𝗖𝗼𝗺𝗽𝗼𝘂𝗻𝗱𝗶𝗻𝗴 𝗖𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝘆
🌌⚙️ SUCCESSOR Ω HARNESS
ACTIVATE. EVOLVE. SCALE.
Powered by TRANSFORMATIONAL INTELLIGENCE ∞.
Imagine an enterprise whose completed work leaves it better equipped for the next mission.
A useful method retained.
A capability checked against fresh requirements.
A new opportunity made practical.
The ambition is a real, constructive loop:
𝗨𝘀𝗲𝗳𝘂𝗹 𝘄𝗼𝗿𝗸 → 𝗩𝗲𝗿𝗶𝗳𝗶𝗲𝗱 𝗿𝗲𝘀𝘂𝗹𝘁 → 𝗥𝗲𝘂𝘀𝗮𝗯𝗹𝗲 𝗰𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝘆 → 𝗕𝗲𝘁𝘁𝗲𝗿 𝘀𝘂𝗯𝘀𝗲𝗾𝘂𝗲𝗻𝘁 𝘄𝗼𝗿𝗸.
🔬 𝗧𝗛𝗘 𝗘𝗩𝗜𝗗𝗘𝗡𝗖𝗘
Our new 18-page research edition examines eight streams, six evaluation windows and four approaches.
All 864 tasks per approach met the declared qualification requirements.
The flywheel used 96.9% fewer counted computational units than the frozen full-mask baseline.
The important distinction:
• Most of that saving came from replacing the method.
• Conventional adaptive caching matched the flywheel exactly.
• The preauthored expert used 26.7% fewer units than the flywheel.
• All eight streams failed the strict acceleration test.
These are synthetic research results, not measured profit or proof of a self-accelerating enterprise.
𝗧𝗛𝗘 𝗙𝗥𝗢𝗡𝗧𝗜𝗘𝗥 𝗜𝗦 𝗦𝗧𝗜𝗟𝗟 𝗢𝗣𝗘𝗡.
Put the strongest methods inside the system.
Make new capabilities earn their construction cost.
Demonstrate that one mission genuinely improves the next.
For users, the standard is simple:
𝗩𝗮𝗹𝘂𝗲 𝘄𝗼𝗿𝘁𝗵 𝘂𝘀𝗶𝗻𝗴 𝘁𝗼𝗱𝗮𝘆.
𝗠𝗲𝗮𝘀𝘂𝗿𝗮𝗯𝗹𝗲 𝗿𝗲𝗮𝘀𝗼𝗻𝘀 𝘁𝗼 𝗿𝗲𝘁𝘂𝗿𝗻 𝘁𝗼𝗺𝗼𝗿𝗿𝗼𝘄.
If we can turn this loop into sustained, verified advantage at scale, MONTREAL.AI could win the AI race. 🏆✨
That is our ambition. It remains to be earned.
📖 Read the paper:
montrealai.github.io/AGI-Alp…
🛠️ Explore the code:
github.com/MontrealAI/AGI-Al…
🌐 Discover the project:
montrealai.github.io/AGI-Alp…
♾️ What should your next mission leave behind besides its result?
solana:tWKHzXd5PRmxTF5cMfJkm2Ua3TcjwNNoSRUqx6Apump #AGIALPHA #TransformationalIntelligence #AutonomousEnterprises #AgenticAI #SUCCESSOR
Québec.AI retweeted
🌌♾️ COMPLETE THE JOB. KEEP THE CAPABILITY.
A finished task is valuable.
The ability to solve the next one can be worth much more.
Introducing THE AUTONOMOUS WORK ECONOMY
From Useful Outcomes to Expanding Productive Capability
The ambition behind AGI ALPHA is simple to understand and demanding to build:
Make useful work the beginning of greater capability.
Deliver an outcome someone needs.
Verify that it works.
Retain what can be reused.
Apply it to a new challenge.
Reinvest available surplus in the next improvement.
🌌♾️ Work creates value. Capability carries it forward.
Our new research preprint explores this vision through executable experiments, a bilingual demonstration and transparent models comparing automated delivery with traditional human-performed work.
One concrete result:
2 of 8 fresh quote-comparison cases solved by the initial capability.
8 of 8 solved by the retained successor.
A conventional specialist also solves all eight. The result demonstrates expanded capability, not superiority over every alternative.
That distinction matters. We want improvements that earn their place, not impressive labels attached to ordinary automation.
The immediate opportunity is to deliver useful outcomes at compelling cost and equivalent quality. The longer-term ambition is to turn the resources generated by that work into greater productive capacity.
The paper investigates that path. Its economics are conditional models, not realized profits; a self-financing, self-accelerating enterprise remains a goal to demonstrate.
We do not need to wait for that future to create value today.
🌌⚙️ SUCCESSOR Ω HARNESS
ACTIVATE. EVOLVE. SCALE.
Powered by TRANSFORMATIONAL INTELLIGENCE ∞.
📄 THE AUTONOMOUS WORK ECONOMY
github.com/MontrealAI/AGI-Al…
What should an AI retain from today’s work to make tomorrow’s work better?
solana:tWKHzXd5PRmxTF5cMfJkm2Ua3TcjwNNoSRUqx6Apump #AGIALPHA #AIAgents #AutonomousWork #TransformationalIntelligence
GitHub: github.com/MontrealAI/AGI-Al…
Québec.AI retweeted
trying to rise to the occasion. grateful to be working with a team that feels the responsibility as deeply as i do.
By the by, if I had to guess, Greg Brockman is currently doing heroic things to fix the internet behind the scenes. And I expect that even though his net contribution to AI safety and resilience will in the final tally be unsurpassable, no one in the space will think to attribute large scale patches to his effort or effort that happens under his leadership.
Québec.AI retweeted
🌌⚙️ 𝗪𝗛𝗔𝗧 𝗜𝗙 𝗧𝗛𝗘 𝗠𝗢𝗦𝗧 𝗩𝗔𝗟𝗨𝗔𝗕𝗟𝗘 𝗢𝗨𝗧𝗣𝗨𝗧 𝗢𝗙 𝗔𝗡 𝗔𝗜 𝗠𝗜𝗦𝗦𝗜𝗢𝗡…
was what it made possible NEXT?
A finished task is an outcome.
A reusable capability is a new starting point.
Introducing our new paper:
𝗦𝗨𝗖𝗖𝗘𝗦𝗦𝗢𝗥 Ω 𝗛𝗔𝗥𝗡𝗘𝗦𝗦
𝗠𝗮𝗸𝗲 𝗘𝘃𝗲𝗿𝘆 𝗠𝗶𝘀𝘀𝗶𝗼𝗻 𝗕𝘂𝗶𝗹𝗱 𝗪𝗵𝗮𝘁 𝗖𝗼𝗺𝗲𝘀 𝗡𝗲𝘅𝘁.
Powered by TRANSFORMATIONAL INTELLIGENCE ∞.
The ambition is practical:
don’t just automate the work.
Build something useful that survives the work.
🔬 𝗪𝗔𝗧𝗖𝗛 𝗜𝗧 𝗛𝗔𝗣𝗣𝗘𝗡
Our runnable research demo constructs a computational measurement instrument.
1️⃣ BUILD.
A parent procedure corrects a constant detector offset.
2️⃣ CHALLENGE.
Introduce drift. The parent reaches its limit.
3️⃣ EVOLVE.
Construct a successor using two bracketing reference measurements, then check it against the changed requirement.
4️⃣ REUSE.
Apply the resulting procedure to different questions: mean response, rise contrast and late endpoint.
5️⃣ INSPECT.
See the evidence, costs, failures and lineage. Simulate a lost response and recover the retained receipt.
You can run it locally.
No wallet. No model API key.
Python and Node.js required.
✨ 𝗧𝗛𝗘 𝗧𝗥𝗔𝗡𝗦𝗙𝗢𝗥𝗠𝗔𝗧𝗜𝗢𝗡
Instead of asking only:
“How can we answer this question?”
Ask:
“What can we construct that makes the next family of questions easier to answer?”
A measurement method.
A calibration procedure.
An executable capability.
Something the next mission can actually use.
📊 𝗧𝗛𝗘 𝗘𝗩𝗜𝗗𝗘𝗡𝗖𝗘, 𝗪𝗜𝗧𝗛𝗢𝗨𝗧 𝗧𝗛𝗘 𝗛𝗬𝗣𝗘
Retained construction used 12.69% fewer counted arithmetic calls than rebuilding.
Conventional caching matched that benefit.
Direct expert construction was slightly cheaper.
All achieved equal coverage at the primary budget.
The demo also exposes a hidden gain error its checks cannot detect. That failure identifies the next useful construction: a calibrated nonzero reference.
These are executed software results with simulated observations, not physical validation, realized profit or a completed production enterprise.
🚀 𝗞𝗡𝗢𝗪𝗟𝗘𝗗𝗚𝗘 𝗖𝗔𝗡 𝗧𝗥𝗔𝗡𝗦𝗙𝗘𝗥.
𝗔𝗨𝗧𝗛𝗢𝗥𝗜𝗧𝗬 𝗠𝗨𝗦𝗧 𝗕𝗘 𝗘𝗔𝗥𝗡𝗘𝗗.
A successor inherits useful work.
It still needs qualification for its own mission.
📖 Read the 28-page paper:
montrealai.github.io/AGI-Alp…
🛠️ Download the demo, LaTeX and evidence:
github.com/MontrealAI/AGI-Al…
𝗗𝗢𝗡’𝗧 𝗝𝗨𝗦𝗧 𝗖𝗢𝗠𝗣𝗟𝗘𝗧𝗘 𝗧𝗛𝗘 𝗠𝗜𝗦𝗦𝗜𝗢𝗡.
𝗕𝗨𝗜𝗟𝗗 𝗪𝗛𝗔𝗧 𝗧𝗛𝗘 𝗡𝗘𝗫𝗧 𝗠𝗜𝗦𝗦𝗜𝗢𝗡 𝗖𝗔𝗡 𝗕𝗘𝗖𝗢𝗠𝗘.
#TransformationalIntelligence solana:tWKHzXd5PRmxTF5cMfJkm2Ua3TcjwNNoSRUqx6Apump #AgenticAI #AutonomousEnterprises
github.com/MontrealAI/AGI-Al…
Québec.AI retweeted
🌌⚙️ 𝗪𝗛𝗔𝗧 𝗜𝗙 𝗧𝗛𝗘 𝗠𝗢𝗦𝗧 𝗩𝗔𝗟𝗨𝗔𝗕𝗟𝗘 𝗢𝗨𝗧𝗣𝗨𝗧 𝗢𝗙 𝗔𝗡 𝗔𝗜 𝗠𝗜𝗦𝗦𝗜𝗢𝗡…
was what it made possible NEXT?
A finished task is an outcome.
A reusable capability is a new starting point.
Introducing our new paper:
𝗦𝗨𝗖𝗖𝗘𝗦𝗦𝗢𝗥 Ω 𝗛𝗔𝗥𝗡𝗘𝗦𝗦
𝗠𝗮𝗸𝗲 𝗘𝘃𝗲𝗿𝘆 𝗠𝗶𝘀𝘀𝗶𝗼𝗻 𝗕𝘂𝗶𝗹𝗱 𝗪𝗵𝗮𝘁 𝗖𝗼𝗺𝗲𝘀 𝗡𝗲𝘅𝘁.
Powered by TRANSFORMATIONAL INTELLIGENCE ∞.
The ambition is practical:
don’t just automate the work.
Build something useful that survives the work.
🔬 𝗪𝗔𝗧𝗖𝗛 𝗜𝗧 𝗛𝗔𝗣𝗣𝗘𝗡
Our runnable research demo constructs a computational measurement instrument.
1️⃣ BUILD.
A parent procedure corrects a constant detector offset.
2️⃣ CHALLENGE.
Introduce drift. The parent reaches its limit.
3️⃣ EVOLVE.
Construct a successor using two bracketing reference measurements, then check it against the changed requirement.
4️⃣ REUSE.
Apply the resulting procedure to different questions: mean response, rise contrast and late endpoint.
5️⃣ INSPECT.
See the evidence, costs, failures and lineage. Simulate a lost response and recover the retained receipt.
You can run it locally.
No wallet. No model API key.
Python and Node.js required.
✨ 𝗧𝗛𝗘 𝗧𝗥𝗔𝗡𝗦𝗙𝗢𝗥𝗠𝗔𝗧𝗜𝗢𝗡
Instead of asking only:
“How can we answer this question?”
Ask:
“What can we construct that makes the next family of questions easier to answer?”
A measurement method.
A calibration procedure.
An executable capability.
Something the next mission can actually use.
📊 𝗧𝗛𝗘 𝗘𝗩𝗜𝗗𝗘𝗡𝗖𝗘, 𝗪𝗜𝗧𝗛𝗢𝗨𝗧 𝗧𝗛𝗘 𝗛𝗬𝗣𝗘
Retained construction used 12.69% fewer counted arithmetic calls than rebuilding.
Conventional caching matched that benefit.
Direct expert construction was slightly cheaper.
All achieved equal coverage at the primary budget.
The demo also exposes a hidden gain error its checks cannot detect. That failure identifies the next useful construction: a calibrated nonzero reference.
These are executed software results with simulated observations, not physical validation, realized profit or a completed production enterprise.
🚀 𝗞𝗡𝗢𝗪𝗟𝗘𝗗𝗚𝗘 𝗖𝗔𝗡 𝗧𝗥𝗔𝗡𝗦𝗙𝗘𝗥.
𝗔𝗨𝗧𝗛𝗢𝗥𝗜𝗧𝗬 𝗠𝗨𝗦𝗧 𝗕𝗘 𝗘𝗔𝗥𝗡𝗘𝗗.
A successor inherits useful work.
It still needs qualification for its own mission.
📖 Read the 28-page paper:
montrealai.github.io/AGI-Alp…
🛠️ Download the demo, LaTeX and evidence:
github.com/MontrealAI/AGI-Al…
𝗗𝗢𝗡’𝗧 𝗝𝗨𝗦𝗧 𝗖𝗢𝗠𝗣𝗟𝗘𝗧𝗘 𝗧𝗛𝗘 𝗠𝗜𝗦𝗦𝗜𝗢𝗡.
𝗕𝗨𝗜𝗟𝗗 𝗪𝗛𝗔𝗧 𝗧𝗛𝗘 𝗡𝗘𝗫𝗧 𝗠𝗜𝗦𝗦𝗜𝗢𝗡 𝗖𝗔𝗡 𝗕𝗘𝗖𝗢𝗠𝗘.
solana:tWKHzXd5PRmxTF5cMfJkm2Ua3TcjwNNoSRUqx6Apump #TransformationalIntelligence #AgenticAI #AutonomousEnterprises
GitHub: github.com/MontrealAI/AGI-Al…
Québec.AI retweeted
🌌⚙️ 𝗪𝗛𝗔𝗧 𝗜𝗙 𝗧𝗛𝗘 𝗠𝗢𝗦𝗧 𝗩𝗔𝗟𝗨𝗔𝗕𝗟𝗘 𝗢𝗨𝗧𝗣𝗨𝗧 𝗢𝗙 𝗔𝗡 𝗔𝗜 𝗠𝗜𝗦𝗦𝗜𝗢𝗡…
was what it made possible NEXT?
A finished task is an outcome.
A reusable capability is a new starting point.
Introducing our new paper:
𝗦𝗨𝗖𝗖𝗘𝗦𝗦𝗢𝗥 Ω 𝗛𝗔𝗥𝗡𝗘𝗦𝗦
𝗠𝗮𝗸𝗲 𝗘𝘃𝗲𝗿𝘆 𝗠𝗶𝘀𝘀𝗶𝗼𝗻 𝗕𝘂𝗶𝗹𝗱 𝗪𝗵𝗮𝘁 𝗖𝗼𝗺𝗲𝘀 𝗡𝗲𝘅𝘁.
Powered by TRANSFORMATIONAL INTELLIGENCE ∞.
The ambition is practical:
don’t just automate the work.
Build something useful that survives the work.
🔬 𝗪𝗔𝗧𝗖𝗛 𝗜𝗧 𝗛𝗔𝗣𝗣𝗘𝗡
Our runnable research demo constructs a computational measurement instrument.
1️⃣ BUILD.
A parent procedure corrects a constant detector offset.
2️⃣ CHALLENGE.
Introduce drift. The parent reaches its limit.
3️⃣ EVOLVE.
Construct a successor using two bracketing reference measurements, then check it against the changed requirement.
4️⃣ REUSE.
Apply the resulting procedure to different questions: mean response, rise contrast and late endpoint.
5️⃣ INSPECT.
See the evidence, costs, failures and lineage. Simulate a lost response and recover the retained receipt.
You can run it locally.
No wallet. No model API key.
Python and Node.js required.
✨ 𝗧𝗛𝗘 𝗧𝗥𝗔𝗡𝗦𝗙𝗢𝗥𝗠𝗔𝗧𝗜𝗢𝗡
Instead of asking only:
“How can we answer this question?”
Ask:
“What can we construct that makes the next family of questions easier to answer?”
A measurement method.
A calibration procedure.
An executable capability.
Something the next mission can actually use.
📊 𝗧𝗛𝗘 𝗘𝗩𝗜𝗗𝗘𝗡𝗖𝗘, 𝗪𝗜𝗧𝗛𝗢𝗨𝗧 𝗧𝗛𝗘 𝗛𝗬𝗣𝗘
Retained construction used 12.69% fewer counted arithmetic calls than rebuilding.
Conventional caching matched that benefit.
Direct expert construction was slightly cheaper.
All achieved equal coverage at the primary budget.
The demo also exposes a hidden gain error its checks cannot detect. That failure identifies the next useful construction: a calibrated nonzero reference.
These are executed software results with simulated observations, not physical validation, realized profit or a completed production enterprise.
🚀 𝗞𝗡𝗢𝗪𝗟𝗘𝗗𝗚𝗘 𝗖𝗔𝗡 𝗧𝗥𝗔𝗡𝗦𝗙𝗘𝗥.
𝗔𝗨𝗧𝗛𝗢𝗥𝗜𝗧𝗬 𝗠𝗨𝗦𝗧 𝗕𝗘 𝗘𝗔𝗥𝗡𝗘𝗗.
A successor inherits useful work.
It still needs qualification for its own mission.
📖 Read the 28-page paper:
montrealai.github.io/AGI-Alp…
🛠️ Download the demo, LaTeX and evidence:
github.com/MontrealAI/AGI-Al…
𝗗𝗢𝗡’𝗧 𝗝𝗨𝗦𝗧 𝗖𝗢𝗠𝗣𝗟𝗘𝗧𝗘 𝗧𝗛𝗘 𝗠𝗜𝗦𝗦𝗜𝗢𝗡.
𝗕𝗨𝗜𝗟𝗗 𝗪𝗛𝗔𝗧 𝗧𝗛𝗘 𝗡𝗘𝗫𝗧 𝗠𝗜𝗦𝗦𝗜𝗢𝗡 𝗖𝗔𝗡 𝗕𝗘𝗖𝗢𝗠𝗘.
#TransformationalIntelligence #AgenticAI #AutonomousEnterprises solana:tWKHzXd5PRmxTF5cMfJkm2Ua3TcjwNNoSRUqx6Apump
github.com/MontrealAI/AGI-Al…
