𝗟𝗟𝗠 𝗦𝗲𝗿𝘃𝗶𝗻𝗴: 𝟳 𝗪𝗮𝘆𝘀 𝘁𝗼 𝗠𝗮𝗸𝗲 𝗜𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝗖𝗵𝗲𝗮𝗽𝗲𝗿1. Continuous batching: Instead of waiting for every sequence in a batch to finish, evict a sequence the moment it emits its stop token and admit a waiting one into the free slot. The GPU stops idling on the longest generation in the group. - Used when traffic is concurrent and output lengths vary widely.2. Paged attention: Store the KV cache in fixed-size blocks that need not be contiguous, the way an operating system pages memory. Removes the need to reserve worst-case contiguous space per sequence. - Used when memory fragmentation, not compute, is what caps concurrency.3. Prefix caching: Reuse the KV cache for a prompt prefix that has already been computed, so a shared system prompt or a long document is encoded once rather than once per request. - Used when many requests begin with identical tokens.4. Speculative decoding: A small draft model proposes several tokens ahead, and the large model verifies them in one forward pass, accepting the longest correct run. Rejected guesses cost a pass that would have happened anyway. - Used when decoding is memory-bandwidth-bound and spare compute is available.5. Quantization: Serve weights, and sometimes activations or the KV cache, at lower precision than training used. Fewer bytes moved per token is directly fewer milliseconds per token. - Used when the model barely fits, or fits with no room for concurrency.6. Prefill-decode disaggregation: Run the compute-bound prefill phase and the bandwidth-bound decode phase on separate pools of hardware, so neither one queues behind the other. - Used when time-to-first-token and inter-token latency have separate targets.7. Multi-query and grouped-query attention: Share key and value heads across query heads so the KV cache holds a fraction of the entries per token. - Used when long contexts or high concurrency make the cache the binding constraint.Three of these buy throughput by keeping the GPU busy (1, 4, 6) and four buy it by moving fewer bytes (2, 3, 5, 7). That split is the whole subject: decoding is limited by memory bandwidth far more often than by arithmetic, which is why shrinking the KV cache tends to pay more than any scheduler change.

BREAKING: Elon Musk just revealed how Tesla Cybercab owners make money while they sleep.It works exactly like Airbnb.You own the car.You add it to Tesla's robotaxi fleet whenever you want.You pull it back with one tap.Tesla has said Cybercab is priced under $30,000 and is targeting ~$0.20 per mile in operating costs, which is a fraction of what human-driven rideshare costs.Going on vacation? Tap once. Your Cybercab starts earning.Back home? Tap again. It's yours.The model is real: Tesla controls the fleet software, sets the pricing, and takes a cut. Owners get the rest.Car ownership just became an income stream, not a depreciation spiral.Is the yield potential enough to justify the price, or does this only work if Tesla's autonomy actually scales?PS: Make the most out of the AI potential:Learn now:- The AI Founders Playbook: https://lnkd.in/dYrHGTEf- The Claude Code Founder Guide: https://lnkd.in/dkfDJDhM- Claude For Finance. The Full Guide: https://lnkd.in/ezuv3mhG- The BCG Slide Playbook for Claude: https://lnkd.in/ezgi2FRe- Anthropic Leaked Product Playbook: https://lnkd.in/e2mzW_he- The McKinsey Slide Playbook for Claude: https://lnkd.in/eyWgkfB6The AI race is accelerating. The real edge is knowing how to use it.Where do you start?

I keep ten tabs pinned while I build. None of them are ChatGPT.(1) models.devContext windows, pricing and rate limits for every model in one queryable database. Faster than opening five pricing pages.(2) artificialanalysis.aiSpeed, cost and quality on the same axes. The latency-vs-price scatter is the one chart worth checking before you commit.(3) build.nvidia.com/models90+ open models running in the browser. You can shape a prompt against a 70B before you rent a single GPU hour.(4) context7.comPipes current library docs into your coding agent, so it stops calling functions that were deprecated eight months ago.(5) deepwiki.comPoints at a GitHub repo and turns it into something you can interrogate. Good for the hour before you decide to adopt a dependency.(6) gitingest.comFlattens a public repo into one file you paste straight into a long-context model. Pairs with the above.(7) smithery.ai/servers7,400+ MCP servers, searchable. The registry that tells you whether the integration you need already exists.(8) epoch.aiCompute, training cost and scaling trends, with the underlying data published. It is the one place I will cite a number from without checking it twice.(9) promptingguide.aiPrompting techniques mapped back to the papers that introduced them. Useful mainly because it shows you what has already been tried.(10) roadmap.shThe AI engineering path, laid out as a dependency graph. Most useful for spotting the thing you skipped.The tooling layer got good faster than the models did, and almost nobody is pricing that in.What is the tab I am missing?

I built a self-evolving code review agent! Most code review agents use the same prompt every time they run. They can review a diff and generate comments, but they do not remember what your team accepted, rejected, or corrected in previous reviews. That means the same mistakes can keep showing up across review cycles. This project adds persistent experiential memory to the review loop. Before every review, the agent retrieves relevant team rules and similar past review trajectories from memory, then uses that context alongside the current diff to generate review comments. After the review, the engineer can accept, reject, or edit every comment. That feedback becomes the learning signal. Accepted comments reinforce useful rules. Rejected comments create lessons about what should not be flagged. Edited comments refine the team’s preferred convention, scope, or wording. The agent adapts non-parametrically. Instead of fine-tuning the underlying model, it improves by storing review outcomes as reusable memory and retrieving the most relevant experience before the next review. Here is how the loop works: • Retrieve: search memory for relevant insights and similar past reviews before generating comments • Review: generate structured feedback using the current diff plus retrieved memory • Human feedback: pause the workflow and collect an accept, reject, or edit decision for every comment • Reflect: convert those decisions into reusable natural-language rules with rationale, polarity, scope, and confidence • Persist: store both the learned insights and complete review trajectory for future retrieval Over time, the agent builds a memory of how your team actually reviews code without retraining the underlying model. The full workflow runs locally with LangGraph, Ollama, BGE embeddings, and a vector database for long-term memory. The interesting part is that every review leaves behind experience that can be retrieved and reused on the next one. I've shared the complete implementation and setup in the comments!

Someone just trained a small AI model and got 44% on ARC-AGI-1 for ~$0.67 🤯 And made the code 100% open-source. He trained it from scratch in 1.5 hours on one RTX 5090. The $0.67 covers training and inference, which is what a 5090 costs to rent for 2 hours. ARC-AGI tests whether a model can solve visual logic puzzles it has never seen before. LLMs struggled with it for years. This 8-layer model matches the leading small models built specifically for the benchmark and beats many LLMs. His previous result went viral and had Lucas Beyer and Jeremy Howard debating it on X. This is the upgrade. The approach is super simple. Each puzzle's before-and-after grids get flattened into a sequence, and a small transformer learns from scratch on those sequences, using both the training puzzles and the eval puzzles with answers hidden. Every puzzle gets its own small learned tag so patterns carry across tasks, and the model is told where each cell sits in the grid instead of reading it as a flat string of text. He stopped making the model predict the puzzle inputs and only trained it on the answers, and the score moved from 40% to 44%. He doesn't know why it helped. Remove the grid position info or the per-puzzle tags and the score drops to about 24%. Train each puzzle in isolation and it drops to 18%. The representation did most of the work. The other small models credited recursive loops for their scores, and nothing here needed them. Combining solved puzzles across multiple runs already gets 55%, so he thinks 65% is reachable with a plain transformer and no new ideas. His take on why nobody did this in 6 years with a million dollar prize on the table: "It's just a transformer with the most obvious representation." Link to the blog and GitHub repo in the comments.

There is usually one prompt in every LLM project that nobody wants to touch. It works. Nobody remembers exactly why. And changing one sentence can suddenly make the whole pipeline worse. DSPy takes a very different approach. Instead of hand-writing and endlessly tuning prompts, you define: What the LLM step should do What inputs it receives What outputs it should produce How success should be measured Then DSPy optimizes the actual prompt against that metric. The core idea is: Program the logic. Let the compiler tune the prompt. DSPy gives you a few important building blocks: Signatures → define the task Modules → define how the model should solve it Programs → compose multiple steps Metrics → define what “good” means Optimizers → improve prompts and few-shot examples automatically This becomes especially useful when you switch models. Instead of manually rewriting prompts for the new model, you can recompile the same DSPy program and optimize it again. The important part, though, is the metric. A weak metric will simply teach the optimizer to become very good at passing the wrong test. I wrote a complete hands-on guide covering DSPy signatures, modules, metrics, MIPROv2, GEPA, evaluation, prompt compilation and where DSPy fits alongside LangChain and LlamaIndex: https://lnkd.in/gapfEJ6Z We help companies and startups build production AI Agents, RAG systems, GenAI applications and Machine Learning solutions. If you are planning an AI project and need consulting, architecture or implementation support: https://lnkd.in/gD62GMD8 Follow Saimadhu Polamuri & Dataaspirant for more practical content on AI, GenAI, RAG, AI Agents and Machine Learning.

🚨 Coimbatore District Police is launching something NEW! Recently, I got an invite from the Hon’ble Superintendent of Police, Coimbatore District, for the launch of their new initiative. It’s a QR-based app that allows people to report crimes and issues around them anonymously. We had a great discussion and got to see the app’s working and operations through a complete walkthrough. We also shared our insights and discussed how we can take this initiative to our neighbourhoods and create more awareness among people. A salute to the Coimbatore District Police and the entire team behind this initiative. 👏🏻 An initiative that truly brings technology, accessibility, and public safety together. #coimbatore #technology #police #applaunch #publicsafety

OrionBelt is the fence an LLM's output needs. OWL ontologies and SKOS vocabularies, edited in the browser, no Java, no Protégé. Delete a class, see what breaks first. Save a bare year as xsd:gYear, never a guessed month and day. Both checks refuse to guess rather than fill the gap themselves. Early repo, BUSL-1.1: free to use, not to resell. #KnowledgeGraph #Ontology #SemanticWeb #AIEngineering

Choosing an LLM provider isn't just about choosing the best model. You're also choosing: • What you pay for • Who serves it • How it scales • How much infrastructure you control I noticed this while adding 3 different providers to Decode (the coding agent I'm building from scratch). Specifically, I picked providers that represent 3 different ways of running inference: 𝟭/ 𝗚𝗲𝗺𝗶𝗻𝗶 → 𝗕𝘂𝘆 𝘁𝗵𝗲 𝗺𝗼𝗱𝗲𝗹 This is the traditional proprietary API approach. Google owns the model and infrastructure. You send requests and pay for usage. I added Gemini because its generous free tier makes it an easy way to experiment with Decode without paying anything. 𝟮/ 𝗢𝗽𝗲𝗻𝗥𝗼𝘂𝘁𝗲𝗿 → 𝗕𝘂𝘆 𝘁𝗵𝗲 𝘀𝗲𝗿𝘃𝗶𝗻𝗴 OpenRouter gives you one interface to a huge pool of models. So you can experiment with different models without worrying about the underlying infrastructure. 3/ Modal → 𝗦𝗲𝗿𝘃𝗲 𝗼𝗽𝗲𝗻-𝘄𝗲𝗶𝗴𝗵𝘁 𝗺𝗼𝗱𝗲𝗹𝘀 𝘆𝗼𝘂𝗿𝘀𝗲𝗹𝗳 This gives you more control over the inference layer. With Modal Endpoints, I can serve Qwen3.6 35B, GLM-5.2, or even swap in my own fine-tuned model. Without changing anything in the agent harness. But the most interesting difference is the economics... Instead of paying per token, you can pay for GPU compute while the workload runs. This is attractive for large, bursty workloads. For example, imagine processing 1,000 documents containing roughly 30M input tokens. Using Sonnet, my napkin math puts that at roughly $97. Process the same workload batched at ~3,000 tokens/sec on a single H200 through Modal, and you're looking at under 3 hours. At $4.54/hour, that's roughly $13 of GPU compute. Of course, serverless GPU inference isn't automatically cheaper. If Decode asks me for confirmation and I leave an H200 sitting idle overnight, I could burn another ~$45 doing absolutely nothing 😂 That's the point… There isn't one "best" way to run inference. • Interactive workloads might favor pay-per-token APIs. • Large asynchronous workloads might favor serverless GPUs. • Fine-tuned models might push you toward serving open weights yourself. Which is why the most important architectural decision I made was keeping all of this outside the agent loop. The loop doesn't know whether it's talking to Gemini, OpenRouter, Modal, or whatever provider I add next. It simply requests the next completion. Agent loop → Model interface → Provider Keep that boundary clean and you can change the infrastructure underneath as your workload changes. I break down exactly how I implemented this inside Decode in Lesson 2 of my open-source Building a Coding Agent From Scratch series. Read it here: https://lnkd.in/d-3NGttv

𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐯𝐞 𝐀𝐈 𝐩𝐫𝐨𝐣𝐞𝐜𝐭𝐬 𝐫𝐚𝐫𝐞𝐥𝐲 𝐟𝐚𝐢𝐥 𝐛𝐞𝐜𝐚𝐮𝐬𝐞 𝐭𝐡𝐞 𝐦𝐨𝐝𝐞𝐥 𝐢𝐬 “𝐧𝐨𝐭 𝐩𝐨𝐰𝐞𝐫𝐟𝐮𝐥 𝐞𝐧𝐨𝐮𝐠𝐡.”𝐓𝐡𝐞𝐲 𝐨𝐟𝐭𝐞𝐧 𝐟𝐚𝐢𝐥 𝐛𝐞𝐜𝐚𝐮𝐬𝐞 𝐭𝐡𝐞 𝐬𝐲𝐬𝐭𝐞𝐦 𝐚𝐫𝐨𝐮𝐧𝐝 𝐭𝐡𝐞 𝐦𝐨𝐝𝐞𝐥 𝐰𝐚𝐬 𝐧𝐞𝐯𝐞𝐫 𝐝𝐞𝐬𝐢𝐠𝐧𝐞𝐝 𝐩𝐫𝐨𝐩𝐞𝐫𝐥𝐲.A practical GenAI project can be thought of as a lifecycle:→ Identify the use caseDefine the problem, users, requirements, and expected outcome.→ Experiment and selectEvaluate available foundation models and choose one that fits the task.→ Adapt, align, and augmentImprove the model with techniques such as fine-tuning, alignment, retrieval, and additional data when needed.→ EvaluateMeasure quality, accuracy, relevance, safety, and other task-specific requirements.→ Deploy and integrateConnect the model to the application, infrastructure, data, and workflows.→ Monitor and improveTrack how the system performs in production and iterate based on what you learn.And this is where things get interesting.A production GenAI application isn’t just:User → LLM → AnswerIt can involve:→ Foundation models→ Prompting→ Retrieval and external knowledge→ Vector databases→ Tools and APIs→ Application logic→ Evaluation→ Monitoring→ Security and governanceThe model is an important component.But the surrounding architecture determines how useful that model becomes.So instead of starting a GenAI project with:“Which model should we use?”Start with:“What problem are we solving, and how will we measure whether it works?”That shift can completely change how you approach GenAI engineering.I created this cheat sheet to make the concepts easier to understand and remember.Save it for your next GenAI project.Follow Vansh for more practical AI engineering breakdowns, architecture maps, cheat sheets, and GenAI concepts.

Over the past few days, I have been exploring how GenAI can move beyond isolated code generation or automation toward end-to-end engineering execution. This led me to architect and develop a unified, domain-agnostic AI Development and test Agent.🚀 Architecture: LLM reasoning + Semantic RAG + context orchestration + tool-based execution, transforming requirements into code, test assets and data, followed by execution, intelligent failure analysis and self-healing.⚙️ Designed with model abstraction, structured outputs, governance and observability, enabling the same architecture to adapt across domains and technology stacks.💼 Business impact: Faster delivery | Lower engineering effort | Higher unit and test coverage | Earlier defect detection | Faster RCA | Greater release confidence.🔄 Understand → Retrieve → Reason → Generate → Execute → Validate → Learn

A founder came to me six weeks ago with a clear vision.He wanted to build an AI-powered recommendation engine for his SaaS product. LLM-based. Personalised to each user. The kind of thing that sounds impressive in a pitch deck and genuinely is impressive when it works.I spent two days going through his data, his user behaviour, his use case, and his team's capacity to maintain what we'd build.Then I told him not to build it.Not because recommendation engines are bad. Not because LLMs can't power them. But because his users were filtering by three variables industry, company size, and budget range. That's it. Three variables. Deterministic logic would return the right answer every time, instantly, cheaply, and with zero maintenance overhead.An LLM would return a plausible answer most of the time, occasionally with confidence it shouldn't have, at 40x the cost per query, with a maintenance burden his two-person engineering team wasn't resourced to handle.He pushed back. Everyone else is adding AI. His competitors are adding AI. His investors expect AI.I asked him one question: "Is the goal to have AI, or to give users the right answer?"He went with the filter. It shipped in three days. It works perfectly.Here are the four situations where I consistently recommend not using AI:When the logic is deterministic. If the right answer can be computed reliably from structured inputs, use a function. Functions don't hallucinate. They don't drift. They don't need evals. They don't have a token budget.When the training data doesn't exist yet. RAG needs clean, current, comprehensive data. Fine-tuning needs labelled examples. If neither exists in usable form, you're not building an AI product, you're building a data collection project disguised as an AI project.When the maintenance cost exceeds the value. Every AI system needs ongoing attention, evals, prompt reviews, model updates, quality monitoring. If the team doesn't have the capacity for that, you're not deploying AI, you're creating debt.When a wrong answer has serious consequences and you can't build adequate safeguards. In regulated industries especially, if the AI being wrong creates legal, financial, or safety risk and you can't build a robust human review layer, the risk isn't worth the capability.The best AI engineers I know are not the ones who find ways to use AI on every problem.They're the ones who know when not to.What's something you built with AI that you later realised didn't need it?

OpenBot is an open-source, self-hostable template for turning agents into governed "AI coworkers" that can actually operate a computer. Each Bot gets its own container, Chromium profile, /workspace, and logins, so agents do not share files or browser sessions. Browser actions, file operations, shell commands, MCP calls, and component use all go through a central gateway that resolves the target, evaluates a CEL policy, records an audit entry, and only then executes or refuses the action.Because a Bot is simply an AG-UI endpoint, agents built with frameworks such as LangGraph, CrewAI, Mastra, Pydantic AI, Google ADK, or custom code can be connected without making OpenBot dependent on one agent framework. Humans can watch the Bot’s live screen, take control for things like login or 2FA, and hand control back afterward.GitHub Repo: https://lnkd.in/gEx8_Aue

▶GenAI vs RAG — The simplest way to understand it 👇Think of it like this:GenAI = A very smart student.RAG = Giving that student an open book before answering.Without RAG:The student answers from memory.Sometimes brilliant. Sometimes confidently wrong.With RAG:The student checks the textbook first.More accurate. More grounded. More reliable.That’s the real difference.Generative AI is the engine that creates content — text, code, images.RAG (Retrieval-Augmented Generation) is an architecture pattern that feeds relevant, up-to-date information into that engine before it responds.In enterprise systems, this difference matters a lot.If you're building:• Internal knowledge assistants• Policy bots• Customer support AI• Domain-specific copilotsUsing only GenAI can lead to hallucinations.Using RAG adds factual grounding.GenAI gives intelligence.RAG gives accuracy.Are you building with memory — or with books? 📘

LLMs are powerful - but not all-knowing.They generate responses based on patterns in training data, not on real-time or domain-specific facts. That’s why Retrieval-Augmented Generation (RAG) matters.RAG improves the accuracy of large language models by pulling in relevant information from external knowledge sources — like your internal documentation, product data, or regulatory content.How does it work?RAG follows a simple but effective pattern:1. Retrieve contextually relevant documents from a vector database2. Feed that content into the model’s prompt3. Let the LLM synthesize a grounded, accurate responseThis approach helps reduce hallucinations and ensures your LLM outputs are based on current, verifiable information — not just whatever the model "remembers" from pretraining.If you're building AI tools that need precision, traceability, and trust — start with RAG. Because context isn’t optional. It’s infrastructure.

knowledge_graph chunks your document, asks a 7B model which concepts relate, then adds an edge between any two names that shared a chunk, right or wrong. Pyvis renders the result as a force-directed network, and networkx layers in degree and community detection so you can rank concepts by importance. Repeated pairs collapse into one edge with summed weight and concatenated relations. 3.7k stars, MIT.

kvcached is a KV-cache memory manager for LLM serving that brings an OS-style virtual-memory idea to GPU memory => instead of forcing engines to reserve a fixed amount of physical GPU memory for KV cache at startup, it separates the cache’s virtual address space from the physical memory backing it, so memory can be allocated and reclaimed as demand changes. This lets multiple LLMs share the same GPU more elastically, supports workloads where models sleep and wake on demand, and makes it easier to colocate inference with training, fine-tuning, or vision jobs. It integrates with vLLM and SGLang and also supports prefix caching.treat KV-cache memory as a dynamic shared resource instead of permanently carving up GPU memory per model.GitHub Repo: https://lnkd.in/gbzMaNMq

Standups exist to answer one question: what's blocked, and by whom. Agent Orchestrator skips the meeting. Every card on its board is a coding agent on its own branch and worktree, and the board reads its status straight from live PR, CI and review state, never from what the agent says about itself. 26 coding agents supported, plans and spawns workers itself. 10.2k stars, Apache-2.0.

Someone just open-sourced an AI whiteboard where you handwrite and it reponds in real time.It's called PenEcho. You sketch equations, draw diagrams, scribble notes wherever they make sense. When you pause, it sends that region to the model. The response appears beside your work. Not in a chat window.On the whiteboard itself.The canvas is 20,000 x 20,000 pixels but only loads tiles where ink exists. Each request costs a few cents. Runs locally. Supports Claude, OpenAI, and Codex.The builder said something that stuck with me: six months ago, no model could have done this. Not the handwriting recognition.The fact that it understands unfinished equations, rough diagrams, and the spatial relationship between them. It infers what you meant from incomplete marks.Link: https://lnkd.in/e9BUnSE2This is how research should work. Think on a whiteboard. AI thinks with you. No copy-pasting into a chat box. No breaking your train of thought.

AI Engineer vs AI Forward Deployed Engineer (AI FDE) 🚀These two roles may become some of the most interesting career paths in the AI industry.But they solve very different problems.👨‍💻 AI EngineerAn AI Engineer primarily focuses on building AI systems.→ Builds RAG applications→ Develops AI Agents→ Integrates LLM APIs→ Creates evaluation pipelines→ Implements guardrails→ Optimizes latency and cost→ Deploys scalable AI applications→ Builds observability and monitoringThink:"I build the AI system."Now imagine a bank approaches your company and says:"We want an AI assistant that can answer questions from millions of internal documents."This is where the AI Forward Deployed Engineer (AI FDE) becomes interesting.🧑‍🚀 AI FDEThe FDE works closely with the customer and discovers the real problem.The bank might say:→ Our documents are spread across SharePoint, databases and internal APIs→ Different employees have different permissions→ Sensitive data cannot leave our infrastructure→ Every AI response needs citations→ Authentication must integrate with our existing systems→ Compliance requires complete audit logsNow someone has to make the AI system actually work inside this environment.That's the AI FDE.They combine:AI Engineering + Software Engineering + Solution Architecture + Customer Engineering + Business UnderstandingThe easiest way I think about these roles:AI Engineer:"I build the AI engine."AI FDE:"I take that engine into the customer's environment and make it solve a real business problem."And that's an important distinction.An AI FDE is not just a consultant.They don't simply create PowerPoints and recommend architectures.They CODE. BUILD. INTEGRATE. DEPLOY. DEBUG.AI Engineers make AI systems possible.AI FDEs make those systems useful in the real world.As enterprise AI adoption grows, I believe we are going to hear a LOT more about Forward Deployed Engineering. 🚀AI Engineer → Build the technologyAI FDE → Make the technology work for the customerKRISHAI Technologies Private Limited