Utilizing Software Features

One of the biggest challenges I see with scaling LLM agents isn’t the model itself. It’s context. Agents break down not because they “can’t think” but because they lose track of what’s happened, what’s been decided, and why. Here’s the pattern I notice: 👉 For short tasks, things work fine. The agent remembers the conversation so far, does its subtasks, and pulls everything together reliably. 👉 But the moment the task gets longer, the context window fills up, and the agent starts forgetting key decisions. That’s when results become inconsistent, and trust breaks down. That’s where Context Engineering comes in. 🔑 Principle 1: Share Full Context, Not Just Results Reliability starts with transparency. If an agent only shares the final outputs of subtasks, the decision-making trail is lost. That makes it impossible to debug or reproduce. You need the full trace, not just the answer. 🔑 Principle 2: Every Action Is an Implicit Decision Every step in a workflow isn’t just “doing the work”, it’s making a decision. And if those decisions conflict because context was lost along the way, you end up with unreliable results. ✨ The Solution to this is "Engineer Smarter Context" It’s not about dumping more history into the next step. It’s about carrying forward the right pieces of context: → Summarize the messy details into something digestible. → Keep the key decisions and turning points visible. → Drop the noise that doesn’t matter. When you do this well, agents can finally handle longer, more complex workflows without falling apart. Reliability doesn’t come from bigger context windows. It comes from smarter context windows. 〰️〰️〰️ Follow me (Aishwarya Srinivasan) for more AI insight and subscribe to my Substack to find more in-depth blogs and weekly updates in AI: https://lnkd.in/dpBNr6Jg

LLM inferencing at scale involves finding the right combination of hardware, software, drivers, kernels, and routing. vLLM handles the engine layer. But when you scale to multiple replicas, a standard load balancer round-robins blind. It has no idea which pod already has your prompt prefix cached or which one has a full queue. That's the gap llm-d fills with its EPP (Endpoint Policy Processor).                                 I put together a 6-part hands-on series that walks through the full stack, running on a MacBook with no GPU needed:                                                               ➡️ Part 1: Deploy vLLM on a local Kubernetes cluster ➡️ Part 2: Add the llm-d gateway and EPP routing layer ➡️ Part 3: Scale to 3 replicas and observe load distribution ➡️ Part 4: Model aliasing with InferenceModelRewrite ➡️ Part 5: Fault tolerance: delete a pod mid-traffic and watch recovery ➡️ Part 6: Scrape EPP Prometheus metrics and watch pool size change live                                 Full series with architecture diagram here: https://lnkd.in/gecdtpSw                                 #llmd #Kubernetes #LLMInference #vLLM #OpenSource #GenAI 

🤖 Coding just got smarter, faster, and more secure. Meet the 5 AI tools transforming software development in 2025! 1️⃣ GitHub Copilot Your ultimate coding assistant, GitHub Copilot. Key Features: 🟣Generates real-time code suggestions 🟣Easy integration with IDEs like VS Code and JetBrains. 🟣Offers custom LLM fine-tuning with personal repositories. Why Use It? 🟣85% of users feel more confident in their code quality. 🟣Tasks are completed 15% faster, with a 55% reduction in task time for Copilot users. 🟣Trusted by 55% of developers and over 50,000 businesses globally. 2️⃣ Cursor IDE A fork of VS Code with GPT-powered AI enhancements Key Features: 🟣Code Generation: Predicts and writes code blocks. 🟣Smart Rewrites: Automatically fixes syntax and formatting. 🟣Cursor Prediction: Anticipates navigation patterns for efficient coding. 🟣Integrated Chatbot: Context-aware guidance and suggestions. Why Use It? Trusted by top organizations like Samsung and OpenAI, Cursor IDE combines advanced AI features with VS Code’s flexibility, making it a strong contender in the AI-powered IDE space. 3️⃣ Tabnine If privacy and data security are a priority, Tabnine is your go-to coding assistant. Built on proprietary and external LLMs, it offers robust code completions. Key Features: 🟣Privacy-Focused: Trained on licensed code with GDPR and SOC-2 compliance. 🟣Transparent Data Use: Shares training data under NDA for added trust. 🟣Flexibility Why Use It? With over 1 million monthly users, Tabnine stands out for prioritizing security without sacrificing productivity. 4️⃣ Warp Terminal A modern twist on the CLI, Warp combines an IDE-like interface with AI-driven features to simplify terminal tasks. Key Features: 🟣Warp AI: Provides natural language command suggestions via ChatGPT. 🟣Agent Mode: Executes commands and resolves errors autonomously. 🟣Smart Command Completion: Suggests time-saving CLI commands. 🟣No-Retention Policy: Ensures complete data privacy. Why Use It? Warp is a game-changer for terminal users, offering features that save time and effort while enhancing productivity. 5️⃣ Replit Agent Replit Agent goes beyond coding assistance, acting as a virtual junior full-stack developer for building and deploying applications. Key Features: 🟣Natural Language Interface: Build complete applications with simple prompts. 🟣Infrastructure Setup: Deploy-ready configurations for various applications. 🟣Iterative Improvements: Add or modify features effortlessly. Why Use It? Although experimental and available in limited access, Replit Agent offers a glimpse into the future of AI-driven development 💡 These tools don’t just save time, they enable developers to focus on what truly matters: solving real-world problems and delivering exceptional products. #AI #SoftwareDevelopment #DeveloperTools #Productivity #TechInnovation

So many AI coding platforms, which one should you go with? The Chocolate Milk Cult spent months rigorously testing various tools for AI-assisted coding flows. We tested tools both individually and with each other across various flows involving codebase QA, targeted changes, building features from scratch, building features within existing code bases, and code review. Tool selection for coding depends on three factors: infrastructure cost versus technical complexity, collaboration dynamics, and context quality for large codebases. I won't bore you with more exposition. Here are the 4 that are worth your money, and how to best use them-- Lovable: When infrastructure cost exceeds technical complexity, use Lovable. If you want to build a simple automation web service or test an idea, the cost of setting up AWS, configuring deployment, linking services together is often higher than the actual technical work. Lovable eliminates that friction. You just build and it handles hosting, infrastructure, everything. Lovable charges a premium for this, but in many such cases, you don’t have to pay 3 SWEs part-time to set up UI, tools, and deploy, so you’re saving a lot of time and mental energy. When technical complexity is high, or when you need consistent iteration, or when multiple people are making changes, switch to other tools. CLI Tools: Claude Code & Codex. The best use of CLI tools is for microservices or standalone services. Write entire functionality end-to-end with Claude Code, spin it up as its own environment. Your larger codebase calls it as a function or service. It doesn’t integrate the code directly. This lets you make sweeping changes within the service without merge fallout in the main codebase. Claude Code is my primary interface. It has a much better search than Codex. Tool use is better, especially when it comes to autonomously working to fix things. UX is cleaner and it has a much faster speed of iteration. Codex has a higher intelligence ceiling. Code reviews are more thorough. Raw capability is higher than Claude Code. But UX is worse. Logs are hard to read. Execution is slower. I use Codex in sub-agents called from Claude Code. You get the ease of use where it matters and the intelligence ceiling when you need it. Augment Code Augment Code has the best context quality for large codebases. Claude Code and Codex are good at context but not great. Augment is better. This makes Augment the right choice for inline changes within large bases. Enterprise settings. Multi-person teams. Inflexible codebases where commits need to be small and controlled. When you’re not going to make large or complex changes per request, Augment handles targeted fixes better than CLI tools. The IDE experience is excellent. Their CLI integration is weaker. Use it in the IDE for scoped questions and targeted changes. For the best AI tools you should use, read: https://lnkd.in/eQh9vSzr

Happy Friday everyone, this week in #learnwithmz let's dive into something close to every developer's heart: 𝐀𝐈 𝐂𝐨𝐝𝐢𝐧𝐠 𝐓𝐨𝐨𝐥𝐬 As AI revolutionizes the way we write, debug, and manage code, it's important to identify which tools truly deliver value. Over the course of two weeks, I tested some of the most popular options by building a full-stack app prototype with each tool. Here's a quick breakdown to help you find the best fit for your specific needs: 🏆 Best Overall: GitHub Copilot Seamless integration with your IDE. Great for inline suggestions and debugging. New Copilot Chat feature allows conversational debugging. Learn more: https://lnkd.in/g4mdv4Ej 💡 Best for Non-Technical Users: Vercel V0 Intuitive and beginner-friendly. Component-specific editing via AI makes prototyping easier. Learn more: https://vercel.com/ 💻 Best for Full-Stack Cloud Development: Replit Ghostwriter Great for collaborative, cloud-based projects. Comes with built-in hosting capabilities. Learn more: https://replit.com/ 🚀 Emerging tool to Watch: Cursor Excellent Copilot alternative. Ideal for agent-driven workflows. Learn more: https://www.cursor.com/ 💎Notable mention: Cline Completely open-source and free alternative to Cursor + Windsurf, available as a lightweight VS Code extension. Enables agent-driven coding with advanced tool integrations. Produces cleaner code with fewer errors and improved self-correction capabilities. Lacks inline chat functionality Learn more: https://lnkd.in/gzESqien 𝐎𝐭𝐡𝐞𝐫𝐬 𝐰𝐨𝐫𝐭𝐡 𝐞𝐱𝐩𝐥𝐨𝐫𝐢𝐧𝐠 - Codeium: Strong AI assistant for codegen and refactoring. https://codeium.com/ - Bolt: Provides cloud-based development https://bolt.new/ - Tempo: PRD-to-Code workflows for designers and devs. Focused on REACT. https://www.tempolabs.ai/ 𝐖𝐡𝐲 𝐀𝐈 𝐂𝐨𝐝𝐢𝐧𝐠 𝐭𝐨𝐨𝐥𝐬 𝐦𝐚𝐭𝐭𝐞𝐫 These tools save time, reduce cognitive load, and empower developers to focus on creative problem-solving. However, the right choice depends on your use case, whether it's prototyping, debugging, or full-stack development. Which AI coding tools are you using? Let me know in the comments, and if you'd like a deeper comparison post! #AI #CodingTools #Developers #TechFriday #LearnAI #learnwithmz P.S. Image is generated via DALL·E

As companies look to scale their GenAI initiatives, a significant hurdle is emerging: the cost of scaling the infrastructure, particularly in managing tokens for paid Large Language Models (LLMs) and the surrounding infrastructure. Here's what companies need to know: a) Token-based pricing, the standard for most LLM providers, presents a significant cost management challenge due to the wide cost variations between models. For instance, GPT-4 can be ten times more expensive than GPT-3.5-turbo. b) Infrastructure costs go beyond just the LLM fees. For every $1 spent on developing a model, companies may need to pay $100 to $1,000 on infrastructure to run it effectively. c) Run costs typically exceed build costs for GenAI applications, with model usage and labor being the most significant drivers. Optimizing costs is an ongoing process, and the following best practices would help reduce the costs significantly: a) Techniques, like preloading embeddings, can reduce query costs from a dollar to less than a penny. b) Optimizing prompts to reduce token usage c) Using task-specific, smaller models where appropriate d) Implementing caching and batching of requests e) Utilizing model quantization and distillation techniques f) A flexible API system can help avoid vendor lock-in and allow quick adaptation as technology evolves. Investments in GenAI should be tied to ROI. Not all AI interactions need the same level of responsiveness (and cost). Leaders must focus on sustainable, cost-effective scaling strategies as we transition from GenAI's 'honeymoon phase'. The key is to balance innovation and financial prudence, ensuring long-term success in the AI-driven future. #GenerativeAI #AIScaling #TechLeadership #InnovationCosts #GenAI

𝗧𝗵𝗲 𝗟𝗟𝗠 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗚𝗮𝗽: 𝗪𝗵𝘆 𝘁𝗵𝗲 𝗠𝗮𝗿𝗸𝗲𝘁 𝗶𝘀 𝗕𝗿𝗼𝗸𝗲𝗻 The current market for LLM serving is broken. Most companies are still trying to force a square peg into a round hole treating Generative AI like a standard REST API. If you want to build or serve LLMs effectively, you have to break your legacy mental models. Traditional DevOps heuristics don't just fail here; they actively burn capital. 𝗧𝗵𝗲 𝗦𝗵𝗶𝗳𝘁: 𝗙𝗿𝗼𝗺 𝗠𝗶𝗰𝗿𝗼𝘀𝗲𝗿𝘃𝗶𝗰𝗲𝘀 𝘁𝗼 𝗧𝗼𝗸𝗲𝗻-𝗡𝗮𝘁𝗶𝘃𝗲 𝗜𝗻𝗳𝗿𝗮 To succeed, your mental model must evolve: ● 𝗧𝗵𝗲 𝗥𝗲𝗾𝘂𝗲𝘀𝘁 𝗶𝘀 𝗻𝗼 𝗹𝗼𝗻𝗴𝗲𝗿 𝘁𝗵𝗲 𝗮𝘁𝗼𝗺𝗶𝗰 𝘂𝗻𝗶𝘁. The 𝗧𝗼𝗸𝗲𝗻 is. ● 𝗠𝗲𝗺𝗼𝗿𝘆 𝗵𝗮𝘀 𝗺𝗼𝘃𝗲𝗱 𝗯𝗲𝘆𝗼𝗻𝗱 𝘀𝘆𝘀𝘁𝗲𝗺 𝗥𝗔𝗠. 𝗩𝗥𝗔𝗠 and 𝗞𝗩 𝗰𝗮𝗰𝗵𝗲 management now define your primary constraints. ● 𝗟𝗮𝘁𝗲𝗻𝗰𝘆 𝗶𝘀𝗻'𝘁 𝗮 𝘀𝗶𝗻𝗴𝗹𝗲 𝗺𝗲𝘁𝗿𝗶𝗰. 𝗧𝗧𝗙𝗧 (Time to First Token) and 𝗧𝗣𝗢𝗧 (Time Per Output Token) require independent optimization paths. ● 𝗔 "𝗥𝗲𝗽𝗹𝗶𝗰𝗮" 𝗶𝘀 𝗿𝗮𝗿𝗲𝗹𝘆 𝗮 𝘀𝗶𝗻𝗴𝗹𝗲 𝗽𝗼𝗱. It is often a 𝗱𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗲𝗱 𝗴𝗿𝗼𝘂𝗽 of heterogeneous resources working in concert. ● 𝗦𝗰𝗮𝗹𝗶𝗻𝗴 𝗶𝘀𝗻'𝘁 𝗶𝗻𝘀𝘁𝗮𝗻𝘁𝗮𝗻𝗲𝗼𝘂𝘀. The sheer weight of 𝗺𝗼𝗱𝗲𝗹 𝗹𝗼𝗮𝗱𝗶𝗻𝗴 creates a massive cold-start bottleneck that standard autoscalers can't handle. ● 𝗖𝗣𝗨 𝗶𝘀 𝗮 𝗻𝗼𝗶𝘀𝘆, 𝘂𝗻𝗿𝗲𝗹𝗶𝗮𝗯𝗹𝗲 𝘀𝗶𝗴𝗻𝗮𝗹. In a GPU-bound world, CPU utilization is no longer a primary scaling metric. ● 𝗟𝗼𝗮𝗱 𝗕𝗮𝗹𝗮𝗻𝗰𝗶𝗻𝗴 𝗺𝘂𝘀𝘁 𝗯𝗲 𝗰𝗼𝗻𝘁𝗲𝘅𝘁-𝗮𝘄𝗮𝗿𝗲. Effective routing requires an understanding of 𝗿𝗲𝗾𝘂𝗲𝘀𝘁 𝗰𝗼𝘀𝘁 and 𝗰𝗮𝗰𝗵𝗲 𝗹𝗼𝗰𝗮𝗹𝗶𝘁𝘆. ● 𝗟𝗼𝗻𝗴 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗶𝘀 𝗮𝗻 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻. It is a deliberate trade-off between model capability and exponential compute costs. 𝗪𝗵𝘆 𝗧𝗵𝗶𝘀 "𝗖𝗹𝗶𝗰𝗸𝘀" Once you accept that LLM serving is a fundamentally different discipline, the "broken" state of the market makes sense. You realize why optimization isn't a luxury, it’s the difference between a viable product and a burning GPU. This shift explains why: 1. 𝘃𝗟𝗟𝗠 and 𝗣𝗮𝗴𝗲𝗱𝗔𝘁𝘁𝗲𝗻𝘁𝗶𝗼𝗻 aren't just tools; they are the new foundation. 2. 𝗤𝘂𝗮𝗻𝘁𝗶𝘇𝗮𝘁𝗶𝗼𝗻 is a capacity strategy, not just a way to save space. 3. 𝗧𝗼𝗽𝗼𝗹𝗼𝗴𝘆-𝗮𝘄𝗮𝗿𝗲 𝘀𝗰𝗵𝗲𝗱𝘂𝗹𝗶𝗻𝗴 is a requirement for performance. 4. 𝗗𝗲𝗰𝗼𝘂𝗽𝗹𝗶𝗻𝗴 𝗣𝗿𝗲𝗳𝗶𝗹𝗹 𝗮𝗻𝗱 𝗗𝗲𝗰𝗼𝗱𝗲 is the only way to scale without wasting 70% of your compute. 𝗧𝗵𝗲 𝗕𝗼𝘁𝘁𝗼𝗺 𝗟𝗶𝗻𝗲:  LLM serving isn't just "deploying a model behind an API." It is the next frontier of 𝗣𝗹𝗮𝘁𝗳𝗼𝗿𝗺 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴. If you treat it like 2015-era microservices, you’ve already lost.

When I started as an SDE, my biggest bottleneck wasn't logic—it was speed. Speed in writing, debugging, and understanding code. Not because I lacked skills. But because I wasn’t using the right tools. If you’re a fresher or early SDE-1, here are some VS Code and IntelliJ extensions I wish I used from day 1 1. VS Code (JavaScript / TypeScript / React folks) ESLint – catches bugs while you write. Prettier – keeps code clean, auto-formats everything. GitLens – understand git history like a pro. Path Intellisense – auto-suggests file paths. Error Lens – highlights errors inline. TabNine / CodeWhisperer – AI suggestions that save keystrokes. IntelliJ (Java / Kotlin / Spring folks) Rainbow Brackets – makes nested code readable. Lombok Plugin – if you're using Lombok, this is a must. Key Promoter X – teaches you shortcuts as you go. Presentation Assistant – displays shortcut hints on screen. SonarLint – live code quality and security analysis. I used to think productivity was about typing faster. Now I know it’s about coding smarter. The tools you use will decide whether you debug for 3 hours or 3 minutes. Keep sharpening the axe 🔨 You'll thank yourself later. Follow Abhay Singh for more such reads. #SoftwareEngineering

One of the most promising directions in software engineering is merging stateful architectures with LLMs to handle complex, multi-step workflows. While LLMs excel at one-step answers, they struggle with multi-hop questions requiring sequential logic and memory. Recent advancements, like O1 Preview’s “chain-of-thought” reasoning, offer a structured approach to multi-step processes, reducing hallucination risks—yet scalability challenges persist. Configuring FSMs (finite state machines) to manage unique workflows remains labor-intensive, limiting scalability. Recent studies address this from various technical approaches: 𝟏. 𝐒𝐭𝐚𝐭𝐞𝐅𝐥𝐨𝐰: This framework organizes multi-step tasks by defining each stage of a process as an FSM state, transitioning based on logical rules or model-driven decisions. For instance, in SQL-based benchmarks, StateFlow drives a linear progression through query parsing, optimization, and validation states. This configuration achieved success rates up to 28% higher on benchmarks like InterCode SQL and task-based datasets. Additionally, StateFlow’s structure delivered substantial cost savings—lowering computation by 5x in SQL tasks and 3x in ALFWorld task workflows—by reducing unnecessary iterations within states. 𝟐. 𝐆𝐮𝐢𝐝𝐞𝐝 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐨𝐧 𝐅𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤𝐬: This method constrains LLM output using regular expressions and context-free grammars (CFGs), enabling strict adherence to syntax rules with minimal overhead. By creating a token-level index for constrained vocabulary, the framework brings token selection to O(1) complexity, allowing rapid selection of context-appropriate outputs while maintaining structural accuracy. For outputs requiring precision, like Python code or JSON, the framework demonstrated a high retention of syntax accuracy without a drop in response speed. 𝟑. 𝐋𝐋𝐌-𝐒𝐀𝐏 (𝐒𝐢𝐭𝐮𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐀𝐰𝐚𝐫𝐞𝐧𝐞𝐬𝐬-𝐁𝐚𝐬𝐞𝐝 𝐏𝐥𝐚𝐧𝐧𝐢𝐧𝐠): This framework combines two LLM agents—LLMgen for FSM generation and LLMeval for iterative evaluation—to refine complex, safety-critical planning tasks. Each plan iteration incorporates feedback on situational awareness, allowing LLM-SAP to anticipate possible hazards and adjust plans accordingly. Tested across 24 hazardous scenarios (e.g., child safety scenarios around household hazards), LLM-SAP achieved an RBS score of 1.21, a notable improvement in handling real-world complexities where safety nuances and interaction dynamics are key. These studies mark progress, but gaps remain. Manual FSM configurations limit scalability, and real-time performance can lag in high-variance environments. LLM-SAP’s multi-agent cycles demand significant resources, limiting rapid adjustments. Yet, the research focus on multi-step reasoning and context responsiveness provides a foundation for scalable LLM-driven architectures—if configuration and resource challenges are resolved.

The rise of Large Language Models (LLMs) has completely changed how developers write, debug, and deploy code. From generating full functions to assisting with documentation, testing, and SQL queries - today’s LLMs are not just tools; they’re intelligent coding partners. These models come in different types - open-source, commercial, coding-specific, and enterprise-grade, each designed for unique developer needs like flexibility, reasoning, and scalability. Here’s a breakdown of 20+ of the best LLMs for coding: Open-Source Models: - Starcoder: Code-focused model built specifically for developers. - Jamba: Scalable mixture-of-experts model for diverse coding tasks. - Falcon: High-performance open-source text generation model. - CODEGEN (Salesforce): Model designed for program synthesis and generation. - Mistral: Lightweight, efficient, and open-weight model. - XGen: Multi-purpose text generation model with strong reasoning abilities. - LLaMA 3: Meta’s open-source advanced reasoning model. - Vicuna: Chat-optimized conversational model tuned for open-source use. - Code LLaMA: Tailored for code generation and completion. - SQLCoder: Specialized in generating and interpreting SQL queries. - CodeTS: Transformer-based model designed for software development. - WizardLM: Instruction-tuned LLM ideal for guided code generation. - Pythia: Research model focused on LLM experimentation and studies. Commercial / Proprietary Models: - Stable Fine-Tuned: For code generation and developer productivity. - Gemini: Google’s multimodal reasoning and problem-solving model. - GPT: The versatile and most widely used language model. - Palm2: Google’s model for structured reasoning and logic tasks. - Claude: Safety-first conversational AI with strong comprehension. - Codex: Powers GitHub Copilot for seamless coding assistance. - CodeBERT: Designed for code search, understanding, and translation. - Qwen: Enterprise AI model with multilingual reasoning capabilities. - Command: Summarization and retrieval model built for business-grade use. Whether you’re writing code, automating tests, or scaling enterprise workflows, the right LLM can supercharge your development speed, accuracy, and creativity. Repost to share with others.