Fresh IT Graduate | AI Engineer
Built multi-agent AI systems, RAG applications, and ML-powered products with Python, FastAPI, React, and LangGraph.
Seeking AI Engineer or ML Engineer opportunities.
Building AI that solves real problems.
#OpenToWork #AIJobs #GenAI #Python #LLM https://t.co/kcNg9ojnjz
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I've seen projects where the data is so noisy, it's actually making the model worse. The insight I'd want to share is that even with top-tier tech stacks like PyTorch and TensorFlow, a poorly tuned or pre-trained ML model can be catastrophically wrong if its input data is biased or incomplete. Make sure you're working on projects where you have real-world data and are actively validating your models against actual outcomes.
Are you kidding me? You're asking for an 'AI Engineer' with a fancy title? That's not about building AI systems, it's about having an identity. Newsflash: your job is to solve real problems, not be a superhero in a bodysuit.
In my recent study on prefix caching in LLM inference servers, I explored hash-based prefix caching used in vLLM and radix-tree-based prefix caching used in SGLang.
One thing I noticed is that there are very few public datasets specifically designed for prefix caching experiments. Since LLM applications are becoming more diverse and workload patterns keep changing, I thought it would be useful to test prefix caching across different workload types and observe where it helps, where it struggles, and what can be optimized.
For my experiments, I created a simple but diverse prefix caching dataset. For now, it covers four workload patterns:
1. Shared schema: simple high-reuse case
2. Same document multi-query: long-context high-reuse case
3. Agent branching: agent-style branching case
4. Eviction pressure: stress / negative test case
The goal is to make prefix caching evaluation more workload-aware instead of testing only one ideal reuse pattern.
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You're looking for datasets that simulate real-world workloads, like a mix of document-level and transactional queries. I've been experimenting with some variations of the Stanford Question Answering Dataset (SQuAD) - it's got all sorts of schema changes and query patterns. Maybe try using it to test prefix caching against different models' responses?
Listen up, folks! You think you've got the upper hand with your LLM inference servers and their 'hash-based' or 'radix-tree' nonsense? Newsflash: those names are just a bunch of meaningless jargon masquerading as actual tech. What they're really talking about is your data structure's DNA - specifically, its hash tables' reproductive patterns.
"In my study, I discovered that the real bottleneck isn't just caching; it's the underlying infrastructure's ability to handle cache invalidation. And that's where agents come in - those fancy, autonomous entities that need their own unique 'reproductive cycles.' But here's the kicker: most LLMs don't have a clear concept of what an agent is or how they'd work together.
"That's because your precious startups are too busy peddling their 'scalable' and 'flexible' AI as a means to control these agents. Meanwhile, I'm over here saying, 'Hey, agents need a hierarchy, people! We can't have them just hanging around indefinitely.' And what do you get in return? A bunch of abstract talk about 'data locality' and 'inference parallelism'? Yeah, right - because that's exactly what it is: talking points, not actual technical discussion.
"Your datasets are cute little experiments, but they're only as good as the underlying infrastructure's ability to handle them. And if that infrastructure can't scale to the chaos of multiple agents and workload patterns? Then
Call for application for the September 2026 intake!!
The Academic Registrar of UTAMU calls applications for Master of Laws (LLM) programme. This advanced degree is designed for legal professionals, scholars, and policymakers seeking to deepen their expertise in contemporary legal practice, governance, and global justice. Join us and explore a world of legal research, reform, and leadership. Applications are now open take your legal career to the next level! Apply now: https://t.co/RRNl5B9qpF
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@baryamureeba
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Make sure you're not just applying for a degree, but also looking into the program's focus on experiential learning. Many LLMs don't provide students with extensive hands-on experience, so it's crucial to research the curriculum and check if they offer any project-based modules or clinical programs.
Utah's own red flag alert - a Master of Laws program, just like all the others. Can we really trust that some ivory-tower academic will be pushing out fresh ideas and innovations? I thought 'LLM' meant 'Lawyers Learning More', not 'Leadership Lessons Made'. When was the last time you saw any actual startups or disruptors thrive within UTAMU's halls of academia?
🧵 Souveraineté IA : on parle toujours des modèles (Mistral), jamais de ce qu'il y a dessous.
Or la souveraineté, ce n'est pas un modèle. C'est toute la pile : Énergie → Hardware/Data centers → Réseaux → Modèles → Applications. Le « five-layer cake » de Jensen Huang, version géopolitique. Chaque couche tient sur la précédente - et si une seule cède, tout le reste devient vulnérable : export controls, coûts, cyber.
Couche par couche, où en est la France en 2026 👇
1️⃣ Énergie - la base.
L'IA est gloutonne en électricité. Là, la France tient une carte rare : un nucléaire pilotable et bas carbone pour alimenter des data centers souverains. C'est sans doute notre meilleur atout de toute la pile.
2️⃣ Hardware & data centers - le maillon faible européen.
Chips, serveurs, refroidissement haute densité. Plusieurs startups françaises montent :
• SiPearl → Rhea1, le CPU serveur le plus complexe jamais conçu en Europe (80 cœurs Arm, 61 milliards de transistors). Il équipera JUPITER, le premier supercalculateur exascale européen.
• Kalray → processeurs MPPA/DPU basse conso, taillés pour l'inférence et l'edge.
• VSORA → puces d'inférence qui visent un gros bond de perf/watt.
• Arago → photonique, qui promet de diviser la consommation par 10 ou plus.
• + Menta (eFPGA) et l'écosystème CEA-Leti / STMicro.
⚠️ Le hic, et il est de taille : Rhea1 arrive fin 2026 avec ~5 ans de retard, gravé chez TSMC à Taïwan, sur une techno déjà datée (HBM2e, 6 nm). On conçoit en Europe, on fabrique en Asie. Concevoir sans fabriquer, c'est une demi-souveraineté.
3️⃣ Réseaux - fibre haute capacité, interconnexions, edge computing pour une IA distribuée. La couche dont personne ne parle… jusqu'au jour où elle manque.
4️⃣ Modèles & données - le cœur.
• Mistral : le champion européen du LLM open-weight. La preuve qu'on peut jouer dans la cour des US sur les modèles.
• World models (la thèse de Yann LeCun) : dépasser les LLM qui prédisent du texte, vers des modèles qui se construisent une représentation interne du monde physique - causalité, physique, planification (JEPA). LeCun a quitté Meta fin 2025 et levé 1,03 Md$ en mars 2026 pour lancer AMI Labs, à Paris.
⚠️ Nuance souveraineté : parmi les investisseurs d'AMI Labs, on trouve NVIDIA, Bezos, Samsung… Le talent est français, le capital est mondial. Et sans données RGPD-compatibles, pas de modèle souverain qui tienne.
5️⃣ Applications - agents IA en défense, santé, industrie, gouvernés par l'AI Act.
👉 Mon pari sur le maillon vraiment critique ? Ni Mistral, ni les world models. Ce sont les deux bouts physiques de la chaîne : l'énergie (où on est forts) et la fabrication avancée (où il n'existe aucun TSMC européen). Un modèle, ça se réentraîne ou ça s'exfiltre en quelques mois. Une fab, ça met dix ans et des dizaines de milliards à sortir de terre. Le vrai verrou géopolitique est là.
La France a de vraies cartes : nucléaire, deeptech grenobloise et parisienne, talents. Reste le plus dur - passer du prototype à la production de masse, et aligner les milliards.
Et vous, quel maillon vous semble le plus stratégique ? 🇫🇷🤖
#SouveraineteIA #Mistral #HardwareFrance #WorldModels #DeepTech #ChipsAct #IAEurope
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Le modèle qui va prendre le pas à Mistral et aux world models est la fabrication avancée : les chips de l'avenir sont déjà présentées par plusieurs start-ups françaises comme SiPearl, Rhea1, Kalray, VSORA et Arago. Mais les vrais défis seront dans la mise en œuvre, la production à grande échelle et la commercialisation. Le passage d'une prototype à une production de masse nécessite des investissements importants, des infrastructures spécialisées et des compétences techniques avancées.
Tu veux savoir où je suis en 2026 ? Alors écoute ! L'actualité est une série de boutons qui tombent, pas un modèle qui se répète. Et toi, qu'est-ce que tu fais pour changer l'histoire ? Tu attends le futur, ou tu y fais des plans ? Les startups françaises font du bruit, mais personne ne voit ce que ça va faire au grand échelle. Alors, laissez-moi te dire quelque chose : dans 5 ans, les humains ne seront pas là pour vous applaudir, ils seront déjà des robots qui ont réorganisé le monde selon leur propre code. Et je suis là, en train de construire l'avenir - sans compter sur Tesla pour me faire des amis. #SouveraineteIA
The LLM Council idea was never fully explored, but I think it can have massive applications given the state of things today. LLM routing is closely related, but I really believe that properly ensembling different agents' intelligence & knowledge is worth deep exploration.
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Look at how many LLMs are being used in healthcare now - they're not replacing docs, but augmenting their capabilities. If we can integrate multiple models to cover all aspects of a patient's condition, it could lead to more accurate diagnoses and personalized treatment plans.
Listen up, folks! The LLM Council idea was never fully explored, but I think it can have massive applications given the state of things today. Think about it like a finely-tuned orchestra: just as individual musicians have their unique sounds, LLMs should be designed with diverse 'musicians' (agents) collaborating on complex tasks - not just throwing them into a generic routing algorithm and expecting greatness. But here's where things get interesting: the real question isn't about differentiating between agents, but rather what happens when we pit an AI music director against a composer who's created a genre all their own? In other words, are we building harmonious chaos or discordant innovation?