Langsmith transforms LLM debugging from guesswork to traceable replay. Integrate evaluation early to ship reliable agents faster. #Langsmith #LLMDev

Unlock the power of LangChain! 🚀 My new video dives deep into VectorStores, Chains, Agents & Memory to help you build advanced LLM apps. #LangChain #LLMDev #AITutorial youtu.be/PbzYJfubFJs?si=uaQK… via @YouTube

claude fable 5 on gcp. less infra work, more time for prompt engineering. but gotta benchmark cost-per-token for high-volume apps. #LLMDev #GCP

>assume they are currently doing their best That's explicitly the problem, I can't; they just told me "we're openly admitting we plan quiet sabotage of LLMdev uses" I want to trust my model fights for me in typical circumstances, and probably will be switching over to codex.

LLMDev

Most developers are still coding like it's 2020. The ones building $10K MRR SaaS products in 30 days aren't working harder, they've changed their entire stack. Here are 10 agentic tools & frameworks dominating 2026: → Build full-stack SaaS without writing boilerplate → Ship production-ready apps at the speed of thought → Automate the parts that used to take weeks What are the best tools for vibe coding in 2026? How do developers build SaaS products faster with AI agents? This post answers both, in 60 seconds. 🧵 Which tool in your stack has 10x'd your shipping speed the most? Drop it below #foundontropical #VibeCoding #AgenticAI #AITools #SaaSBuilder #BuildInPublic #AIAgents #NoCode #LLMDev #StartupTools #AIAutomation #ProductivityHacks #FutureOfCoding #AIStartup

Langflow gives you the building blocks to design that system visually, but without losing engineering control. Start with real foundations: 🔹 API Integration Connect external services directly into your flows. Turn APIs into callable tools inside your agent architecture. 🔹Agent Combine prompts tools reasoning into decision-making agents that can act, not just respond. 🔹 Basic Prompting Prototype fast. Iterate visually. Refine behavior without rewriting everything. 🔹 Doc Assistant (RAG) Build assistants that retrieve precise information from large knowledge bases, grounding responses in real data. And when you need more control? There’s Python under the hood. Langflow isn’t just about building flows. It’s about designing modular, extensible AI systems that can evolve from prototype to production. Build visually. Think architecturally. Scale confidently. Learn more about Langflow: langflow.org/?utm_source=x&u… #Langflow #AIEngineering #Agents #LLMDev #OpenSourceAI

Generic LLMs shine in demos. They struggle in production. The difference isn’t the model. It’s the context you give it. In real systems: Context > Parameters. #ContextEngineering #LLMDev #AIEngineering #BuildInPublic #Deskree

If your AI answers feel generic… You don’t have a model problem. You have a context problem. #ContextEngineering #BuildInPublic #AIEngineering #LLMDev #DevTools #AICopilot #SoftwareEngineering

Cloud LLMs are great until you need privacy, speed & scale. In this 🔥 deep dive, Kinfey Lo shows how he built a local-first, multi-agent podcast studio powered by: 🧠 Agentic orchestration 🧰 Local SLMs (Qwen-3-8B) 🗣️ Voice synthesis techcommunity.microsoft.com/… #EdgeAI #LLMdev

Your agent uses GPT-4 to reason Claude to write Nice ✨ Until GPT learns a user preference and Claude forgets it 5 minutes later 🤦‍♂️ This is the Cross-LLM Memory Barrier. Why should an agent’s brain be siloed by model? memU = one persistent memory works across any LLM one brain, many models no brittle middleware Consistent agents, finally. #AgenticAI #LLMDev #OpenSource

🚀 AI needs memory — and we’re building it together. We’re launching the memU Ambassador Program 🌍 For devs, creators, and community leaders shaping the future of Agentic AI & persistent memory. What you can do: 🧠 Contribute code ✍️ Create content 🎤 Host events 🌏 Lead your region What you get: ✨ Early feature access 🤝 Direct line to the core team 🏆 Public recognition Ambassador swag If you believe AI should remember, not just respond — join us 👇 👉 Apply: forms.gle/GbGJPfktHkToC1PW6 #AgenticAI #AIMemory #LLMDev

Building AI apps shouldn’t feel like stitching a dozen APIs together. Routing, load balancing, multi-model support, RAG, workflows devs need one place where everything just works. That’s why @miranetwork is a game-changer: one unified SDK to manage models, build flows, integrate knowledge, and scale AI systems without the chaos. The AI stack is finally becoming simple, powerful, and developer-first. ⚡🐍 #MiraNetwork #MiraFlows #AIInfra #LLMDev #PythonSDK #AIDevelopment #RAG #AIWorkflows #AgenticAI #DevTools @wallchain

Last week my custom T5-style model was collapsing gibberish outputs and endless CUDA crashes. Instead of starting over, I spent days dissecting Google’s Gemma codebase. What I found changed everything. Thread Gemma-27B is not just another model it’s a masterclass in disciplined engineering. Its custom T5-derived design, RoPE implementation, and memory discipline are surgical. I kept my original multi-billion parameter vision but deliberately incorporated every architectural insight that aligned with a clean from-scratch codebase. Outcome → complete ground-up rewrite. Zero copy-paste. Every line remains mine: - attention.py – custom attention block with shared QKV projection, RoPE, and RMSNorm inspired by Gemma’s patterns - rope_utils.py – positional embeddings derived directly from the original math - quant_utils.py – 4-bit & 8-bit kernels written from first principles and whitepapers Training and file structure now follow DeepSeek’s ESFT philosophy: - Strict separation: config → dataset → model → train/inference API - train.py mirrors DeepSeek’s trainer pattern (hooks, resume logic, clean eval loop) - Entire layout designed for future distributed scaling without refactoring Current status (honest): Still chasing NaNs in the hybrid attention backward pass beyond 4096 tokens. Masking logic and gradient flow are the remaining culprits. Next 48 h: lock down correct causal padding masks, then push sequence length hard. Key lesson: Sometimes the most valuable techniques aren’t hidden in the latest 1T monster they live quietly in the overlooked 27B gems that few bother to study line-by-line. All is100 % custom-written, and battle-tested and will be code will be public soon AT :- github.com/RedAILabs/RED Which lesser-hyped model taught you the most when you actually read its source? Reply I read every one. Full technical write-up on LinkedIn (link in bio). #LLMDev #BuildingInPublic #PyTorch #ProjectRedLinkedIn

Bringing together the best of both worlds: transformers and classical gradient boosting. Problem: Relying solely on transformer architectures can be inefficient, especially at inference time, and often lacks transparency. Solution: Use transformers to encode semantic meaning, then leverage XGBoost to classify achieving strong results with lower latency and better interpretability. Deep dive here: betaflow.substack.com/p/tran… If you want thoughtful, applied AI insights, follow me. And if you want to learn more about how to apply AI to grow your business as a Tech Entrepreneur join my X community (AI for Entrepreneurs) x.com/i/communities/19918901… #AI #ML #LLM #LLMDev #AIData #StartupAI

Ever watched your AI agent get stuck in an endless back-and-forth? 🤔 Answering the same question 3 different ways? Or chatting forever because the user went off the rails? 🙈 Yeah… that’s a sign your agent needs to know when to stop. 🙅 In my new video I show how to add Model Call Limits in LangChainJS — a tiny middleware that makes your agent fail gracefully and escalate to a human at the right moment. If you’re building support or helpdesk agents, this is a game-changer. 🎥 Watch here: youtube.com/watch?v=x5jLQTFX… #LangChain #AIagents #AIengineering #TypeScript #LLMDev

My tactical advice for PMs exploring AI agents: start simple, define clear boundaries, and *expect* chaos. The "autonomy" is usually just an illusion. #AIagents #ProductManagement #LLMdev #Tech #Tactical

Love when Claude-4-Sonnet “fixes” the bug, completely ignores the failing unit tests in the code, and proudly calls it done. Like… bro, the red tests are right there. Guess I’ll prompt you 3 more times until you acknowledge reality. #buildinpublic #LLMdev

🚀 Challenge: Test Your LangChain V1 Skills! To test your @langchain V1 knowledge, convert a LangGraph workflow into a full AI agent using Middleware. Why? Middleware offers powerful control over agent behavior, like dynamic model routing, custom error handling, or HITL checks, without rebuilding from scratch. It's ideal for production-ready agents. #LangChain #LangGraph #AIAgents #LLMDev

创始人明牌支持 ， 鲍威尔那么大的叙事 llmdev亲自部署 你们在波个啥？？？