CTO / VP Engineering | Real-Time Systems, Simulation, 3D, AI & Robotics | Ex-Airbus → Scaling Eng Orgs | Paris 🇫🇷
Paris, France
Joined January 2015
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120.
Artificial intelligence has made the world almost perfect.
But what happens when perfection begins to erase the things that make us human?
A reflection on intelligence, freedom and the future.
GRAIN DE SABLE — Fragments of 2120.
teapawt.net/cto_software/status/21…
2120. Every answer can arrive at the right moment.
But what if a human being wants a moment that belongs to no system?
Is hesitation a defect—or a form of freedom?
#GrainDeSable #HumanAgency
2120. Every answer can arrive at the right moment.
But what if a human being wants a moment that belongs to no system?
Is hesitation a defect—or a form of freedom?
#GrainDeSable #HumanAgency
2120. The world is almost perfect.
No catastrophe. No visible enemy. Just a future that has learned to make life beautifully smooth.
What might be missing?
GRAIN DE SABLE — an unpublished novel.
#GrainDeSable #HumanAgency
teapawt.net/cto_software/status/21…
AI governance starts to matter when it leaves the policy document and enters the operating model.
Clear ownership. Explicit authority. Defined escalation paths. Data boundaries. Runtime visibility. Production criteria.
That is what turns governance from a control layer into an execution capability.
Governance creates velocity when it makes boundaries explicit.
dataversity.net/articles/to-…
#AIGovernance #AgenticAI #EngineeringLeadership #OperatingModel #AILeadership
Jean-Christophe Huc retweeted
LLMs understand language.
The next generation of AI needs to understand the world.
That is why world models matter.
A language model predicts what token comes next.
A world model tries to predict:
What happens if I move this object?
What happens if the robot turns left?
What happens if something falls?
What action produces the desired result?
This sounds like a subtle distinction.
It isn't.
It is one of the bridges between generative AI and physical intelligence.
Robots cannot rely on memorized patterns alone.
Reality constantly produces situations they have never seen before.
Different lighting.
Different objects.
Different environments.
Different humans.
The goal is no longer simply to generate the right answer.
It is to predict the consequences of an action.
Chatbots need language models. Robots need a model of reality.
#AI #WorldModels #Robotics #PhysicalAI
Follow @ingliguori.
Responsible AI becomes real when responsibility is executable at runtime.
Once agents become non-human identities with permissions, tools and machine-to-machine access, governance can no longer live only in policy.
It has to exist in the system: identity, authority, observability, revocation and recovery.
You cannot govern what you cannot see — or interrupt.
Redefining Corporate Responsibility For The AI-Powered Tech Stack via @forbes forbes.com/councils/forbeste…
NO83 / Decision Note #01
I’m building NO83, an experimental crypto decision system.
The goal is not to “predict the market”.
It is to answer a harder question:
How do you make better decisions when the future remains uncertain?
One principle became fundamental very quickly:
A winning trade can still be a bad decision.
A losing trade can still be a good one.
At T0, NO83 only knows:
• signals
• market context
• competing hypotheses
• risk
The outcome comes later.
So the system keeps two things strictly separate:
WHAT WE KNEW WHEN WE DECIDED
from
WHAT HAPPENED AFTER
If you judge a decision using information that only existed afterwards, you are not learning.
You are rewriting history.
Decision quality ≠ outcome quality.
The model predicts. The system has to know what to do when the prediction is wrong.
Chatbots were only the beginning.
The next AI frontier is not about generating better text.
It’s about understanding how the physical world works.
World Models can learn from video, sensors and multimodal data to predict outcomes, simulate environments and plan actions.
That changes the game for:
🤖 Robotics
🚗 Autonomous systems
🌍 Simulation
🏭 Industrial automation
The next breakthrough in AI may not be a model that talks better.
It may be a model that can reason about reality.
Do you think World Models will unlock reliable general-purpose robotics sooner than expected?
Follow @ingliguori for more insights on AI, innovation and emerging technologies.
#AI #WorldModels #Robotics #PhysicalAI
If robotics benchmarks start getting zero-shot commoditized, the moat moves.
From benchmark capability → to real-world robustness.
From model performance → to adaptation, verification and recovery.
Component performance ≠ system performance.
AI does not fix an ambiguous operating model. It amplifies it.
Better models won’t compensate for unclear ownership, slow decision rights or broken handoffs.
The real leverage comes from redesigning how decisions, accountability and workflows operate around AI.
Governance, done well, creates velocity.
The key to #AI value is hiding in plain sight >> Your operating model
buff.ly/fq5ovLu
#tech #innovation #disruption #leadership #business #management #governance #organization #operatingmodel #CIO #CTO #CDO #CEO #COO #CFO #ROI #valuecreation #AIStrategy #digitalstrategy #digitaltransformation @McKinsey @BetaMoroney @ChuckDBrooks @MikeFlache @Khulood_Almani @IngridVasiliu @Fabriziobustama @Timothy_Hughes @NafisAlam @YuHelenYu @RLDI_Lamy @MCLynd @OlKonol_oa @RigneySec @BenRothke @HWillert @AndrewinContact @TerenceLeungSF @Shi4Tech @enilev @Nicochan33 @SabineVdL @bimedotcom @IanLJones98 @FrRonconii @RiccardoBua @Eli_Krumova @NYIke @HaroldSinnott @SallyEaves @GlobalIQX @AntGrasso @AkwyZ @DinisGuarda @PVynckier @YvesMulkers @HaleChris @BillMew @RobMay70 @NigelTozer @m49D4ch3lly @Ronald_vanLoon @jeancayeux @NevilleGaunt @asokan_telecom @cybersecboardrm @RVP @QuePasaChico
Nous avons construit une application mobile intégrant un modèle IA.
Nous cherchons maintenant la bonne personne pour la reprendre. Nous avons conçu et développé Shoot Tracker, une application mobile destinée aux tireurs sportifs, capable de détecter et d’analyser automatiquement les impacts sur cible.
Le projet a été construit à deux :
expertise métier / usages du tir sportif
conception technique, développement mobile, computer vision et pipeline ML de mon côté
Le produit permet notamment :
• détection des impacts en temps réel sur la cible
• analyse d’une photo de cible déjà tirée
• correction manuelle des impacts détectés
• calcul du scoring
• analyse des séquences de tir pour faciliter la correction
• gestion complète des séances de tir
• historisation des résultats (par arme)
• statistiques de performance et de progression
Sous le capot, le produit combine :
• Flutter / Dart Android
• détection d’objets avec TensorFlow
• traitement d’image OpenCV / C++
• keypoint matching et homographie
• pipeline MLOps en Python pour les datasets, l’entraînement et le versioning des modèles
L’application a été testée sur des centaines de cibles réelles, avec une précision de détection comprise aujourd’hui entre 85 % et 95 % selon les conditions de prise de vue.
Une boucle complète d’amélioration est déjà en place : les images terrain peuvent être collectées, revues, ajoutées au dataset puis réutilisées pour entraîner de nouvelles versions du modèle.
Le produit est fonctionnel.
Et nous souhaitons maintenant trouver un entrepreneur, une entreprise ou une équipe produit capable de l’emmener plus loin :
optimisation IA, internationalisation et lancement commercial.
La reprise peut inclure l’application mobile, le code source, le pipeline ML, les modèles entraînés, les datasets et le savoir-faire technique associé.
Si vous connaissez quelqu’un actif dans le tir sportif, la computer vision, les applications mobiles sportives ou l’IA appliquée, nous sommes preneurs d’une mise en relation.
Message privé bienvenu.
#IA #ComputerVision #MobileApp #MLOps #SportTech
