Dharan TD’s Post

Most AI projects don’t fail because of bad models. They fail because no one controls decision-making. => So what you get is: – prompts duct-taped together – zero visibility into why the system does what it does – outputs that look good in demos and fall apart in production -- * That’s not an AI system. * That’s a slot machine. * I don’t build that. * I design and build systems that: --- – structure decision-making – make reasoning traceable – expose hidden assumptions – stay stable under real-world pressure === What I actually do: • Architect AI systems beyond prompt engineering • Design agent workflows and reasoning pipelines • Build distributed backends (Elixir, OTP, real-time) • Turn messy inputs into deterministic decision layers • Make decisions auditable --- Consulting: • $300–500/hour (standard) • $500+/hour (high-impact / strategic) --- This isn’t brainstorming. We work on real problems that affect production systems. --- Who this is for: Teams that: – are already technically strong – are hitting the limits of current AI tooling – are done with demos and want real systems === I don’t take many projects. If you’re building something that has to actually work, this will make sense. --- #AI #ArtificialIntelligence #LLM #AIEngineering #SystemDesign #DistributedSystems #Elixir #Startups #DecisionMaking #AIArchitecture

Open models or proprietary AI? Most teams are asking the wrong question. It is a product strategy decision. Use proprietary models when you need: the fastest path to a polished MVP frontier quality right away broad multimodal capabilities without building infra first Use open models when you need: tighter data privacy and deployment control lower marginal cost at scale deeper customization for your workflow infrastructure that enterprises can approve The part many teams miss: A lot of enterprises simply cannot send sensitive data to externally hosted LLMs. In those environments, self-hosted or tightly controlled deployments are not a preference. They are a requirement. My view: The best architecture is usually hybrid. Start with proprietary models to learn fast. Move stable, sensitive, or high-volume workflows to open models when control and economics start to matter more. Route different jobs to different models instead of forcing one model to do everything. Examples: Open models: Llama 4, Qwen 3.5, Gemma 3, Kimi 2.5, DeepSeek V3.2 Proprietary models: GPT-5.4, Claude Sonnet 4.6, Gemini 3.1 Pro, Grok 4 The real moat is not “we use open” or “we use closed.” The moat is knowing which workloads belong where. #AI #GenAI #OpenSourceAI #LLM #ProductManagement #AIProductManagement #EnterpriseAI #Startups #MachineLearning #TechStrategy

This video perfectly explains what’s happening in AI right now… and it’s uncomfortable. 😬 Everyone says: AI companies with software engineers. But the reality looks like this 👇 Slow preparation. Over engineered steps. Too many tools. Too much complexity. Meanwhile… Lean AI teams are shipping faster, simpler, and smarter. ⚡ The old model: • Large engineering teams • Months of planning • Endless architecture debates • Heavy infrastructure The new model: • Small AI first teams • Prompt driven development • Rapid iteration • Ship in days, not quarters The biggest shift isn’t AI replacing engineers. It’s AI changing how much engineering you actually need. The companies that win will: • Reduce complexity • Move faster • Build lighter • Think AI first, not code first This is the moment where speed beats size. Where clarity beats process. Where builders beat planners. 🚀 Most teams are still preparing the shrimp. A few are already serving the dish. Which one are you? 👀 Follow Anish Anurag for more AI and tech insights. #AI #ArtificialIntelligence #SoftwareEngineering #Startup #Tech #AIFirst #ProductDevelopment #NoCode #Founders #FutureOfWork

There is a big difference between "AI-generated" and "engineering-ready." Most teams find that out the hard way. I have watched capable engineering teams celebrate AI output too early. The code looks clean. The demo runs smoothly. Everyone feels like they are moving fast. Then it hits production. Suddenly the context is missing. The edge cases were never tested. The architectural fit was assumed, not verified. And nobody can fully explain why certain decisions were made because the AI made them quietly and the team moved on. Gartner estimates that by 2025, 40% of AI engineering projects will fail not because of bad generation but because of insufficient human oversight in the pipeline. Forrester backs this up, noting that production-grade AI code requires an average of three to four human review cycles before it meets enterprise deployment standards. That gap between generated and ready is exactly where most teams are losing time, quality, and trust right now. Code is not the unit of success. Engineering-ready work is. That means context, validation, testing, approval, and verified fit with the existing system. Not as a bureaucratic layer but as the actual definition of done. This is what a proper AI Development Life Cycle gets right. Work does not just move forward because it was produced. It moves forward because it was checked, understood, and signed off by someone who owns the outcome. That is not slowing AI down. That is what makes AI safe enough to trust at scale. Happy to discuss how teams are implementing this in practice, especially those navigating enterprise delivery in regulated environments across Europe. #AIDLC #DigitalTransformation #EnterpriseAI #ProductionEngineering

Website Link: www.systemdrd.com 🚀 Most AI systems don’t break at the model level — they break at the context layer. We’re entering the era of Context Engineering. I’ve been building a production-grade Context Manager that brings enterprise-level discipline to how AI systems handle context windows — something most demos completely ignore. Here’s what it includes: ⚙️ Intelligent Token Counter → Real-time prompt token tracking using tiktoken 🧠 Multi-Strategy Summarizer → Extractive, abstractive, and hybrid compression techniques 📊 Context Window Optimizer → Automatic pruning, filtering, and prioritization 🖥️ React Dashboard → Live monitoring of efficiency metrics and compression ratios 💡 Key insight: As LLMs scale, context becomes the new bottleneck — not compute, not models. Whoever masters context will build the most efficient, cost-effective, and scalable AI systems. Curious — how are you managing context in your AI stack? #AI #ContextEngineering #LLM #SystemDesign #AIArchitecture #GenerativeAI #SoftwareEngineering #MachineLearning #TechLeadership #AIProducts #Startups #Innovation

Accelerating Vertical AI: From "Wrapper" to Core Infrastructure The "AI gold rush" has a dirty secret: most companies are stuck in the experimentation phase. They’ve built wrappers, they’ve run pilots, but they haven’t achieved True AI Velocity. They are scrambling. At FoamLabs AI, we see the bottleneck. It’s not the models, it’s the engineering. To build vertically and dominate a specific industry, you need more than a prompt; you need a production-ready backbone that solves critical integration challenges. We are here to scale you from your existing baseline, to a future that your company envisions. 🏗️ Solving the Engineering Gap General-purpose AI is easy. Vertical AI is hard. We focus on the three pillars that move the needle: Data Plumbing at Scale: Most enterprise data is trapped in silos. We build the modular integration layers that turn fragmented data into high-fidelity context, cutting implementation time from months to weeks. Hardening the Stack: "It worked in the sandbox" doesn't cut it. We solve for latency optimization, smart model-routing, and cost-efficient scaling so your AI performs under real-world pressure. Domain-Specific Alignment: Whether it’s FinTech, HealthTech, or complex Logistics, we deploy RAG pipelines and fine-tuning strategies that ensure AI speaks your industry’s language—accurately and compliantly. 🚀 What is True AI Velocity? It’s the ability to iterate, deploy, and scale without breaking your core infrastructure. It’s moving from "human-in-the-loop" to AI-orchestrated workflows that deliver measurable ROI. The window to lead your vertical is open. Don't let technical debt or integration friction hold you back. Let’s stop talking about AI potential and start delivering true AI performance. #AI #Engineering #VerticalAI #FoamLabsAI #Automation #EnterpriseTech

🔥 We just kicked off a high-impact AI infrastructure build. This is not another AI experiment. This is production-grade execution. 🚀 Project EP002 is now live ⚡ Focus: Scalable, GPU-powered AI systems built for real workloads Here’s what’s being engineered: 🔹 High-performance GPU architecture → Designed for intensive training & inference 🔹 End-to-end AI pipelines → Data → models → deployment → optimization 🔹 Scalable infrastructure layer → Built to handle growth, not break under it 🔹 Performance-first design → Speed, efficiency, and cost control 🤖 Reality: AI without compute power is just theory. Serious systems demand serious infrastructure. Most teams ignore this layer. That’s exactly why they fail to scale. At www.cyblico.com we focus on what actually drives AI forward: Robust systems, not surface-level tools. 🔥 EP002 sets the foundation for next-gen AI deployment. More to come. — Team CYBLICO #AI #GPU #AIAgents #Automation #UKTech #Startups #CloudComputing #Innovation #TechLeadership #DigitalTransformation

Most companies treating AI as a vendor relationship are building on sand. Mistral just launched Forge — a platform that lets enterprises train frontier-grade models directly on their own data. Pre-training on internal datasets, reinforcement learning aligned to company-specific policies, mixture-of-experts architectures for large-scale deployment. ASML, the European Space Agency, Ericsson. These aren't scrappy startups experimenting with prompts. They're encoding decades of proprietary knowledge — terminology, workflows, compliance requirements — directly into model behavior. Here's the uncomfortable truth: the companies winning with AI right now aren't winning because they picked a better API. They're winning because they turned their internal data into a model that no competitor can replicate. Generic public models are a commodity. Every company using the same GPT wrapper is competing on execution alone, and that's a brutal place to be. The real moat has always been what you know that others don't. The only question is whether you've structured that knowledge in a way a model can learn from it. Three years from now, the companies that treated AI like a SaaS subscription will look a lot like companies that outsourced their entire engineering team. What's stopping your company from treating proprietary data as a model training asset rather than just a database? #AI #EnterpriseAI #MachineLearning #Startups #ArtificialIntelligence #TechLeadership #LLM Join Agentic Engineering Club → t.me/villson_hub

Most teams still think AI maturity = better prompting. That’s Layer 1 thinking. And it’s exactly why most “AI initiatives” never make it past demos. The teams actually shipping durable AI systems in 2026 are climbing 4 engineering layers: 1) Prompt Engineering Useful for fast wins. But nothing compounds. 2) Context Engineering This is where your company becomes legible to machines. Memory, RAG, tool access, structured retrieval. 3) Harness Engineering The model is not the system. The orchestration around it is. Validation, retries, observability, routing, guardrails. 4) Intent Engineering The hardest layer. Encoding what the system should optimize for when instructions run out: trust trade-offs business outcomes long-term user value The biggest shift I’m seeing: The winning AI companies are no longer model-first. They’re becoming systems-first organizations. Because the ceiling of your AI product is rarely the model. It’s: how knowledge flows how failure is contained how feedback compounds how purpose is encoded That’s the real moat. The future won’t belong to teams with the best prompts. It’ll belong to teams with the best AI operating systems. Carousel breaks down all 4 layers ↓ #AI #AIAgents #ContextEngineering #AgenticAI #SystemDesign #Founders #Startups #ProductEngineering #MachineLearning #BuildInPublic

If you're building AI systems and stuck on something, I can probably help. Here are 5 things I can turn around in 15 minutes or less. 1. Review your RAG architecture and tell you where it's likely to break. Most retrieval pipelines have the same 3-4 failure points. Chunking strategy, retrieval precision, re-ranking logic, context window misuse. Send me your setup and I'll tell you which one is quietly killing your accuracy. 2. Diagnose why your LLM responses are slow. Latency problems in LLM systems are almost always one of four things, model size, retrieval bottleneck, prompt bloat, or infrastructure misconfiguration. I can usually spot which one from a brief description. 3. Sanity-check your evaluation framework. If you're measuring your AI system's quality with vibes and spot-checks, you're flying blind. I'll tell you what metrics actually matter for your use case and what you're probably missing. 4. Help you decide whether you actually need an LLM for your problem. Genuinely, sometimes you don't. A well-tuned classifier or a structured query pipeline will outperform a bloated LLM setup at a fraction of the cost. I'll give you an honest read. 5. Look at your prompt and tell you why it's underperforming. Bad prompts are the silent killers of otherwise good AI systems. Structure, context injection, instruction clarity, small changes here have outsized impact. Send it over. No pitch. No upsell. I've spent 10 years building these systems. Helping someone think through a problem clearly costs me 15 minutes and might save them weeks. DM me or drop your question in the comments. #ArtificialIntelligence #MachineLearning #Technology #Startups #Innovation