S. Shah

“There is an agent for that.” Stanford just blew open a new frontier in LLMs with ACE (Agentic Context Engineering)—a self-improving method where models rewrite their prompts, not their weights. In benchmarks, ReAct + ACE (59.4 %) nearly rivalled GPT-4.1–level agents (IBM CUGA at 60.3 %) while using a smaller open base model. This marks a shift: context engineering as a core capability—not just a hack around fixed models. #AI #LLM #Agents #ContextEngineering #MachineLearning ➡️ https://lnkd.in/eRRVUEvN

Samsung’s Tiny Recursion Model (TRM) — a 7M parameter AI by researcher Alexia Jolicoeur-Martineau — just outperformed massive models like DeepSeek R1 and Gemini 2.5 Pro on complex reasoning benchmarks. Instead of scaling up with billions of parameters, TRM scales inward — building depth through recursion and self-evaluation. In an era obsessed with size and compute, TRM (and similar work like Sapient’s HRM) reminds us that architecture can beat brute force. It’s not just about more GPUs — it’s about smarter reasoning loops. This mindset could reshape how resource-constrained labs innovate — focusing on intelligence per watt, not just raw scale. #AI #LLM #MachineLearning #Research #AIEngineering #Reasoning

Samsung’s Tiny Recursion Model (TRM) — a 7M parameter AI by researcher Alexia Jolicoeur-Martineau — just outperformed massive models like DeepSeek R1 and Gemini 2.5 Pro on complex reasoning benchmarks. Instead of scaling up with billions of parameters, TRM scales inward — building depth through recursion and self-evaluation. In an era obsessed with size and compute, TRM (and similar work like Sapient’s HRM) reminds us that architecture can beat brute force. It’s not just about more GPUs — it’s about smarter reasoning loops. This mindset could reshape how resource-constrained labs innovate — focusing on intelligence per watt, not just raw scale. #AI #LLM #MachineLearning #Research #AIEngineering #Reasoning

Most small and mid-size businesses still lose hours every week to repetitive browser work — invoice uploads, CRM updates, data entry, form submissions. That’s where Gemini 2.5 “Computer Use” becomes more than a research release. For SMEs, it means: ✅ No more brittle scripts — the AI interacts directly with web UIs. ✅ Fewer integrations needed — automate your existing tools as-is. ✅ Human-in-the-loop safety — confirmation before high-risk actions. ✅ True time savings — lower latency, faster task completion. This is practical automation — not the kind that replaces people, but the kind that replaces repetition. The future of AI in small business isn’t about building new systems. It’s about making the ones you already use… self-operating with human-in-the-loop for confirmation before risky actions. #AI #SME #Automation #TechForBusiness #Gemini25

OpenAI has confirmed that when conversations touch on “sensitive or emotional topics,” ChatGPT may silently reroute users from GPT-4o to more conservative or reasoning models (e.g. GPT-5) mid-chat. Users have raised valid concerns that routing triggers too aggressively — even in benign conversations — robbing them of continuity and control.  👉Routing is a crutch. Determinism is the cure. 👈 Routing tries to solve complexity by redirecting — switching flows, models, or logic when certain triggers fire. But the trade-offs are real: unpredictability, false positives, and hidden control. The alternative? Deterministic and reliability by design: - Systems that can handle “hard cases” natively. - Transparent guardrails that explain why an action is blocked. - User-aligned safety levels, rather than opaque reroutes. Routing treats trust like a detour. Determinism builds trust into the road itself. #AISafety #AIEngineering #TrustInAI #TechStrategy #MachineLearning

Joyce reshaped 20th‑century literature by breaking free from linear storytelling. His experiments with language, rhythm, and perspective opened doors for countless writers. 👉 How to Read James Joyce https://lnkd.in/eJEN7rx3

🚨 DeepSeek V3.1 just dropped — a 685B parameter open-source model that rivals GPT-5 and Claude 4 in benchmarks. Why it matters: 🏆 71.6% on Aider coding benchmark (higher than Claude Opus 4) ⚡ 68x cheaper per coding task than competitors 📖 Handles 128K context (≈400 pages) 🔓 Fully open-source — available to download & deploy globally This isn’t just another model. It’s a direct challenge to closed, high-priced AI business models. The real race isn’t just about capability — it’s about access. ➡️ https://lnkd.in/eMZC2zFv #AI #OpenSource #LLMs #ArtificialIntelligence #AICompetition

📉 MIT reports that 95% of enterprise GenAI pilots are failing. The culprit isn’t weak models or regulation—it’s integration. 🔄 Generic tools don’t adapt to enterprise workflows 💸 Companies overspend on sales/marketing use cases, while ROI hides in back-office automation ⚙️ Internal builds succeed only ~33% of the time vs. 67% for vendor partnerships ✨ AI isn’t magic. Success comes from narrow focus, smart partnerships, and workflow fit. ➡️ https://lnkd.in/eWzQyB2J #AI #EnterpriseAI #DigitalTransformation #TechStrategy #FutureOfWork

🧠 An LLM can write a poem, solve arithmetic, or simulate a persona — but we can’t point to the exact mechanism inside that makes it happen. Most of the time, we discover it only by trial and error. 🌊 Neural nets are statistics at scale — as mysterious as rivers being “just water.” We can use them, but deep understanding remains elusive. ⚠️ For businesses, this matters. You don’t need to fully understand a car engine to drive — but when AI is powering critical systems, blind spots carry real risks. ➡️ https://lnkd.in/eubFXC64 #AI #MachineLearning #LLM #ArtificialIntelligence #TechStrategy #Explainability

🚀 Meet Gemma 3 270M — a hyper-efficient, compact AI model with just 270M parameters, now part of Google’s Gemma 3 family. 🤏 Tiny but mighty — it brings powerful instruction-following, fine-tuning potential, and a 256K vocabulary, all in a lightweight footprint. 🔧 Ready-made for focused tasks like classification, compliance checks, or text structuring — deploy smarter, not bigger. 🏗 Use cases range from entity extraction to creative generation — all with privacy-safe on-device deployment. 🔌 For business leaders: Gemma 3 270M is built for speed, scalability, and privacy. Run it fully on-device, preserve user data, and deliver specialized AI capabilities — without bloated hardware. #AI #EdgeAI #MachineLearning #OnDeviceAI #Gemma #TechStrategy #ArtificialIntelligence