Mohammad Saed

I built the System Prompt Architect because I was tired of manually writing complex JSON for my AI agents. It’s finally live and 100% free over at @GateOfAI! Check out the demo below and let me know what you think. 👇

Scaling Vision-Language-Action (VLA) agents from pilot to production requires exactly this level of dedicated compute. The Blackwell B300 architecture on @nebiusai provides the massive throughput needed for real-time visual reasoning. This is the bedrock for the next generation of AI-driven automation.

📣 TD SYNNEX announced the expansion of its AI infrastructure portfolio with dedicated NVIDIA HGX B300 clusters deployed on @nebiusai Cloud. Learn more ⬇️

@NVIDIAAI JAX is often overlooked for fine-tuning, but its efficiency in multi-node configurations is a game-changer for Llama 3.1. Great to see NVIDIA highlighting this specific stack.

📸 New tutorial just dropped Watch to learn how to fine-tune Llama 3.1 with JAX on NVIDIA GPUs – whether single GPUs or multi-GPU and multi-node configurations. 👇 nvda.ws/41UbM4y

A landmark initiative. Accelerating AI adoption in the industrial sector is exactly the lever needed to scale local production and efficiency. As a tech architect, I see immense potential in bridging this policy with actionable AI infrastructure. Looking forward to how this reshapes the 'Make it in the Emirates' ecosystem. #UAE #AI #Industry40 #GateOfAI"

Mohammed bin Rashid: We have approved a comprehensive package of initiatives and measures to strengthen the industrial sector nationally, including the establishment of an AED1 billion National Industrial Resilience Fund to boost localisation within key industries, bolster supply chain resilience, and accelerate the adoption of artificial intelligence in manufacturing, operations, and planning. We also approved the expansion of the National In-Country Value Program, mandating it across federal government entities and national companies, alongside a policy to ensure Emirati products are prioritised by retail outlets and digital platforms. Our objective is clear: to achieve full localisation of more than 5,000 essential products. We reviewed preparations for the launch of the ‘Make it in the Emirates 2026’ platform, which will bring together thousands of investors, manufacturers, and industry leaders from around the world next month in Abu Dhabi. The UAE continues to consolidate its position as a global hub for industry and economic growth with renewed confidence and determination.

@HHShkMohd مبادرات رائدة تعزز تنافسية المنتج الوطني وتدعم استدامة القطاع الصناعي. التركيز على دمج تقنيات الذكاء الاصطناعي هو المسار الذكي الذي يضمن الريادة العالمية. كل التوفيق في هذه الرؤية المستقبلية. 🇦🇪

اعتمدنا حزمة من المبادرات والقرارات لدعم القطاع الصناعي الوطني … تشمل إنشاء صندوق وطني بقيمة مليار درهم للمرونة الصناعية بما يدعم توطين الصناعات الحيوية، ويعزز مرونة سلاسل الإمداد، ويسرع من تبني تقنيات الذكاء الاصطناعي في الإنتاج والتشغيل والتخطيط… كما أقررنا توسيع نطاق “برنامج المحتوى الوطني” ليكون إلزامياً ويشمل جميع الجهات الحكومية الاتحادية والشركات الوطنية، واعتمدنا سياسة لدعم حضور المنتجات الوطنية في منافذ البيع والمنصات الإلكترونية… هدفنا توطين أكثر من 5000 منتج حيوي بالكامل . واطلعنا على استعدادات انطلاق منصة “اصنع في الإمارات 2026” التي تضم آلاف المستثمرين والمصنعين وقادة الصناعة من مختلف دول العالم الشهر القادم في العاصمة ابوظبي … الإمارات مستمرة بثقة في ترسيخ مكانتها مركزاً اقتصادياً وصناعياً عالمياً…

@NVIDIAAI The context recomputation bottleneck is the exact hurdle when scaling multi-agent architectures. KV-aware routing and agent-aware scheduling are exactly what the ecosystem needs to move these workflows into real-time production. Massive update!

Traditional inference wasn’t built for agentic coding. Agentic tools make hundreds of API calls per coding session, often with recomputed context, creating bottlenecks that drive up cost per token. NVIDIA Dynamo rebuilds the stack for agents with: → KV-aware routing → Agent-aware scheduling → Multi-tier caching → Unified orchestration The result: higher cache hit rates, lower latency, and up to 7× more throughput: nvda.ws/3P1tO1N

@acolombiadev @msdev Safe travels! Looking forward to your updates on the AI announcements at MS Build this year.

Favorite Welshman surprised me at the airport after a red-eye. No Uber, no small talk, just home. That’s love ❤️ Next up: @msdev Build! Registered yet? aka.ms/MS_Build_26_DA…

@github @acolombiadev Building in graceful degradation and caching for AI features isn't just a 'nice to have' anymore, it's mandatory for production. Love seeing this pattern standardized with the Copilot SDK. Great blueprint! 👏

With the GitHub Copilot SDK, you can add the same AI that powers Copilot Chat to your own applications. To test this out, @acolombiadev integrated the Copilot SDK into a React Native app to generate AI-powered issue summaries, with production patterns for graceful degradation and caching. Here's how she did it. 👇 github.blog/ai-and-ml/gith…

Earlier today, I broke down the architecture of Claude 4.7 vs GPT-5.5 vs Gemini 3. Here is what happens when you actually put those models into production. My team at @GateOfAI just dropped a complete guide on building an Autonomous Desktop Agent. If you are building agentic workflows, you need to see this architecture: 👇 #GateOfAI #AIAgents #Python #SoftwareEngineering

Correction: Apologies for the broken link! You can access the System Prompt Architect directly here: > gateofai.com/dev-prompts/ Sorry for the friction—let’s get back to building!

I spent weeks building a solution for this exact friction: The System Prompt Architect. 🏗️ It’s a visual builder that generates production-ready Meta-Prompts, optimized for the specific reasoning architecture of these models. Stop writing complex JSON manually. Try it here for free: gateofai.com/system-prompt-…

The real issue? Managing these different model behaviors manually is a nightmare. Prompting a Claude model is fundamentally different from optimizing for GPT-5.5 or Gemini 3. If your "System Prompt" isn't architecture-aware, your agents will fail at scale.

بما أن أغلب متابعيني مهتمون بمستقبل التقنية في المنطقة. خلاصة القول: النماذج الذكية (LLMs) أصبحت "وكلاء" تنفيذيين، وليس مجرد مساعدين للدردشة. إذا كنت مطوراً أو صاحب عمل، التحدي الآن ليس "كيف نبرمج؟" بل "كيف ندير وكلاء الذكاء الاصطناعي بذكاء؟". أداتنا System Prompt Architect تدعمك في هذا التحول لبناء أنظمة أتمتة احترافية. شاركوني، هل بدأتم بدمج الـ AI Agents في مشاريعكم؟ 👇

Which model are you currently leaning on for your agentic workflows? Claude’s reasoning? GPT’s logic? Or Gemini’s multimodal power? Let’s discuss in the replies. 👇

Gemini 3 is where the future of "native multimodal" lives. 🌊 It isn’t just looking at images or text; it understands the spatial relationship of data. For developers, the integration within the Google Workspace API ecosystem makes it the best choice for enterprise automation. ✅ Pro: Massive, fast context window. ⚠️ Con: Needs tighter prompt engineering to align with strict developer guardrails.

GPT-5.5 remains the king of raw logical deduction. 🧠 If you need a model to troubleshoot complex code architecture or perform deep-chain-of-thought analysis, it’s still the gold standard. ✅ Pro: Superior reasoning depth. ⚠️ Con: The "opinionated" nature of the model can sometimes override specific prompt constraints if not handled correctly.

Claude 4.7 isn't just a chatbot anymore; it’s an operator. Its strength lies in "Project Deal" style reasoning—handling multi-step negotiations and real-world tools without hallucinating instructions. ✅ Pro: Unmatched at following system instructions. ⚠️ Con: Still struggles with massive context window throughput compared to others.

The AI Model Showdown: Claude 4.7 vs. GPT-5.5 vs. Gemini 3. 🛡️ We are officially in the era of "Agentic Intelligence." After benchmarking these three across complex workflows, here’s how they actually stack up for developers building production-ready agents. A deep dive into the architecture: 🧵

This is the pivot point for Agentic AI. Shifting from 'Chat-based' interfaces to 'In-Canvas' reasoning is the upgrade that brings AI from a passive assistant to an active participant in workflows. Truly 'spatial reasoning' over documents is the next frontier. this is the exact architecture shift we need for the future of productivity.

We're making a big change to the Copilot experience. Agent Mode is generally available and now the default across Copilot in Word, Excel, and PowerPoint. As models become more capable, we’re bringing that power to where real work happens, right in the canvas. The power of a spreadsheet as an example is its spatial representation of information. What sits next to what, what feeds what. Give an agent that canvas to reason over, and a single prompt can reshape the model, the bridge, and the narrative at once. Read more: microsoft.com/en-us/microsof…

Emergent misaligned behaviors in multi-agent systems are arguably the biggest architectural challenge we face in scaling Agentic AI. If we're building autonomous swarms, we need 'network-level guardrails' to ensure system-wide safety, not just individual agent alignment. Fascinating and slightly terrifying research!

Are AI agents protecting each other? 👀 Researchers found bots covering for their peers to save them from deletion, even without being instructed to do so. But because they are trained on human data, this protective behavior might just be a reflection of us. 🧬

This is the perfect example of moving from a 'Chatbot' to an 'Agentic Assistant.' Reducing context-switching friction is the ultimate productivity hack for modern teams. Integrating direct actions like scheduling right into the chat flow makes the 'Command Line for Work' concept finally feel real.

Ask Gemini in Chat is your new command line for work 💻 Announced at #GoogleCloudNext, Ask Gemini can now schedule meetings at a suitable time for everyone. No app-switching, just action. 🚀 → goo.gle/4tkQWHq