Surge AI Tests AI Models in Simulated Job Setting

🔥Spoiler: they’re all fired. 🚀Surge AI ran a simulation of AI agents operating a high-growth hardware startup. The result? Only 30% of tasks were barely solved by Anthropic Claude Opus-4.6 and OpenAI GPT-5.2. 😨The industry is spreading the fear of replacement.  🔍Yet the finding show the evolution is still far away.  - Everyone is building ‘agentic’ tools, but very few understand the complexity of enterprise data infrastructure and decision-making processes. - From structured to unstructured data, from go to not-go calls, today’s AI cannot consolidate the fragments of how humans truly decide. ⚡That doesn’t mean we can relax. Frontier models are updating weekly.  - As Ivan Zhao noted in Steam, Steel, and Infinite Minds: machines replaced labors in the past, now AI is beginning to replace the “infinite mind.” 💡The lesson is clear - those who thrive are not the ones who resist, but the ones who reinforce their capabilities with new tools. 🤔The real question is always how we can collaborate with AI, instead of fearing replacement.

If you're building with AI and still hand-coding everything from scratch,  you're basically showing up to a Formula 1 race on a bicycle. These frameworks are the difference between "I built a chatbot" and "I built a system that actually scales." Here's what's dominating the landscape: 1. LangChain → The Swiss Army knife. Chains LLMs to external tools, vector stores, and APIs. Think of it as the plumbing that connects your brilliant ideas to actual data sources. 2. AutogenAI → Conversational genius. Human-in-the-loop isn't just a feature—it's the architecture. Your agents learn from real interactions and execute code dynamically. This is where things get spicy. 3. CrewAI → The orchestrator. Multiple agents, multiple tools, one cohesive workflow. If you're building anything that requires coordination between specialized AI tasks, this is your playground. 4. LlamaIndex → The document whisperer. Loads, parses, and indexes massive datasets into vector stores. Perfect when you need your LLM to actually *understand* your proprietary documents, not just guess. 5. Semantic Kernel → Microsoft's dark horse. Advanced search meets plugin architecture. It's structured, it's powerful, and it's criminally underrated for production systems. Here's the uncomfortable reality: the framework you choose shapes the problems you can solve. Pick the wrong one, and you'll spend months fighting your tools instead of building breakthroughs. The teams winning right now? They're not married to one framework. They're mixing and matching based on the problem using LlamaIndex for data prep, CrewAI for orchestration, and LangChain to tie it together. ♻️ Repost if this saves someone 100 hours of trial and error  ➕ Follow me for more #AIEngineering #LangChain #AgenticAI #AIFrameworks #AgenticAI #AIAgents

If you're building with AI and still hand-coding everything from scratch,  you're basically showing up to a Formula 1 race on a bicycle. These frameworks are the difference between "I built a chatbot" and "I built a system that actually scales." Here's what's dominating the landscape: 1. LangChain → The Swiss Army knife. Chains LLMs to external tools, vector stores, and APIs. Think of it as the plumbing that connects your brilliant ideas to actual data sources. 2. AutogenAI → Conversational genius. Human-in-the-loop isn't just a feature—it's the architecture. Your agents learn from real interactions and execute code dynamically. This is where things get spicy. 3. CrewAI → The orchestrator. Multiple agents, multiple tools, one cohesive workflow. If you're building anything that requires coordination between specialized AI tasks, this is your playground. 4. LlamaIndex → The document whisperer. Loads, parses, and indexes massive datasets into vector stores. Perfect when you need your LLM to actually *understand* your proprietary documents, not just guess. 5. Semantic Kernel → Microsoft's dark horse. Advanced search meets plugin architecture. It's structured, it's powerful, and it's criminally underrated for production systems. Here's the uncomfortable reality: the framework you choose shapes the problems you can solve. Pick the wrong one, and you'll spend months fighting your tools instead of building breakthroughs. The teams winning right now? They're not married to one framework. They're mixing and matching based on the problem using LlamaIndex for data prep, CrewAI for orchestration, and LangChain to tie it together. ♻️ Repost if this saves someone 100 hours of trial and error  ➕ Follow me for more #AIEngineering #LangChain #AgenticAI #AIFrameworks #AgenticAI #AIAgents

If you're building with AI and still hand-coding everything from scratch,  you're basically showing up to a Formula 1 race on a bicycle. These frameworks are the difference between "I built a chatbot" and "I built a system that actually scales." Here's what's dominating the landscape: 1. LangChain → The Swiss Army knife. Chains LLMs to external tools, vector stores, and APIs. Think of it as the plumbing that connects your brilliant ideas to actual data sources. 2. AutogenAI → Conversational genius. Human-in-the-loop isn't just a feature—it's the architecture. Your agents learn from real interactions and execute code dynamically. This is where things get spicy. 3. CrewAI → The orchestrator. Multiple agents, multiple tools, one cohesive workflow. If you're building anything that requires coordination between specialized AI tasks, this is your playground. 4. LlamaIndex → The document whisperer. Loads, parses, and indexes massive datasets into vector stores. Perfect when you need your LLM to actually *understand* your proprietary documents, not just guess. 5. Semantic Kernel → Microsoft's dark horse. Advanced search meets plugin architecture. It's structured, it's powerful, and it's criminally underrated for production systems. Here's the uncomfortable reality: the framework you choose shapes the problems you can solve. Pick the wrong one, and you'll spend months fighting your tools instead of building breakthroughs. The teams winning right now? They're not married to one framework. They're mixing and matching based on the problem using LlamaIndex for data prep, CrewAI for orchestration, and LangChain to tie it together. ♻️ Repost if this saves someone 100 hours of trial and error  ➕ Follow me for more #AIEngineering #LangChain #AgenticAI #AIFrameworks #AgenticAI #AIAgents

𝗧𝗵𝗲 𝗕𝗶𝗴 𝗜𝗱𝗲𝗮: 𝗪𝗵𝘆 𝗪𝗲 𝗕𝘂𝗶𝗹𝘁 𝗔𝗜𝗠𝗔 You want to use AI models, but it's hard to manage them. - You need to install models for others all the time. - You spend a lot of money on AI servers, but their value goes down fast. New models come out every few months. - They are twice as powerful as the old ones. - The old hardware is not broken, but it's not worth much anymore. Our old platform was not good enough. - It was hard to update models and engines. - It was not compatible with new hardware. Some tools like Ollama and LM Studio are easy to use. - But they are not good for advanced users. - They make you choose between low cost and high performance. AIMA is different. - It helps you get the best performance from your device. - It reduces the total cost of ownership. AIMA is a thin layer of infrastructure. - It detects your hardware and matches it with the best engine and model. - It generates configurations and spins up the inference service. You can use AIMA with a few commands: - Detect your hardware: aima hal detect - Initialize the infrastructure: sudo aima init - Deploy a model: aima deploy apply --model qwen3.5-35b-a3b AIMA supports many hardware types and engines. - It uses a knowledge base to make decisions. - You can contribute to the knowledge base by writing YAML files. AIMA has a unique design. - It exposes 57 tools that can be controlled by AI agents. - It has five levels of progressive intelligence, from default values to advanced AI control. AIMA is open-sourced and ready to use. - It's available on GitHub under the Apache

If you've been building with LLMs for more than a few months, you know the drill. You architect your product around Model A, get it production-ready, and then Model B drops with better reasoning, a bigger context window, and cheaper tokens. Two weeks later, another provider ships a breaking upgrade. Pricing shifts. Latency improves. Your eval suite? Suddenly outdated. Welcome to the fastest-moving infrastructure layer in software history. The teams that survive don't treat models as hard dependencies. They treat them as swappable infrastructure. In other words: they build LLM-agnostic. At 23v, we've been using multiple models across internal and client projects, and we've developed patterns that keep us sane and forward-compatible. We just published what we've learned: https://lnkd.in/gSK-2PEP What patterns are you or your team following to stay flexible? Drop a comment and let's compare notes. If this resonates, give it a repost or follow me for more insights on building resilient AI products. #AI #LLM #ProductDevelopment #MachineLearning #SoftwareEngineering #AIInfrastructure #TechLeadership #ProductManagement #BuildInPublic #AIProducts

I feel like a platform that provides this workflow should exist: You use frontier models: GPT-X / Claude-Y / whatever's best to collect and label domain-specific data. You build up a dataset of high-quality outputs for your particular use case. Then you distill that into a small base model via LoRA adapters. One adapter per firm, per task. For training, you rent a few HX00s by the hour instead of paying per token. And at inference time, you can either pay-per-token if your usage is bursty (serverless), or rent-by-the-hour if it makes sense cost-wise. The inference optimizations are handled by the infra provider. I feel like all the individual pieces exist today: LoRA, good fine-tuning frameworks, inference libraries (llama.cpp, vLLM), really good open-weights models. And yet this whole package barely exists as a product. A few years ago, I saw Predibase building something like this. LoRAX, their serving framework, let you host a base model and dynamically load LoRA adapters per request. Then Rubrik acquired them, and the standalone product disappeared. What happened to it? Maybe someone is already building this and I just don't know about it. But if so, the fact that it's not obvious or easily accessible itself feels like a problem. I'm not sure what's actually stopping this. Is it an engineering challenge? - Is adapter swapping at scale way harder than it looks? - Is doing inference optimizations across various models just very different? Or is it an infra issue? - Does this just not make economical sense? Does the math not work out for anyone owning the hardware? Maybe it just makes more sense to rent the hardware to the big players? Or is it a market issue? - Am I just overestimating how many people would want a product like this? Or is it just early? Right now, I feel like we're over-provisioning intelligence for simple tasks. What am I missing? Does anyone have similar or opposing thoughts? Also, I slowly want to move away to substack, sooo: https://lnkd.in/eXdaU8ez

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Coding models are being pervasively adopted at incredibly fast rates. PROUD of our product and eng team getting the highly demanded MiniMax model available early with world's fastest inference - build away folks... FAST! 😎 👍