I applied for this job using AI. Here's what I built. Opendoor's challenge: "Apply to this AI Ops Engineer role using ONLY AI and we'll fast-track you to interview." So, I built a fully autonomous job application agent from scratch in less than 24 hours. The Architecture Each layer uses the right model for the job: → Claude Haiku — JD parsing + form field mapping (classification, lowest cost) → Claude Sonnet — skill gap analysis + cover letter (reasoning required) → Claude Opus — tailored resume generation (quality matters here) → Playwright — browser automation, form filling, file uploads The Hard Engineering Problems 🔹 ATS form detection — built a generic extractor capturing role="combobox", role="radiogroup", hidden file inputs, and ARIA widgets across Rippling, Greenhouse, Lever and Workday 🔹 Two-stage field mapping — 34 deterministic rules handle known fields (name, email, pronouns, EEO) with zero API cost. Only custom/unknown fields go to Claude. Eliminates hallucination where it matters most 🔹 Combobox option reading — options don't exist in the DOM until activated. Built a function that reads aria-controls, opens the field, reads up to 50 real options, matches via exact → prefix → partial priority 🔹 Hidden file inputs — 3-attempt uploader: direct set_input_files → JS visibility injection → Playwright CDP file chooser intercept 🔹 Human-in-the-loop checkpoint — agent pauses after submission for Cloudflare verification, then resumes on terminal Enter 🔹 Full audit trail — every Claude prompt, response, and field fill logged to structured markdown. Full replay of every decision Skill Gap Analysis Before generating documents, the agent classifies every JD requirement as HAVE / PARTIAL / MISSING — and suggests a concrete project with deliverable and first step to close each gap. Cost per run: ~$0.20. Runtime: under 3 minutes. What's Next → ATS-specific adapter modules with pre-mapped selectors → Run-to-run field pattern learning to eliminate repeat LLM calls → Reliability benchmarking across ATS platforms → Checkpoint/retry recovery for dynamic DOM and session failures Code drops once the next improvements land. Follow to stay updated. #AIEngineering #LLM #Anthropic #Claude #Playwright #AgentArchitecture #Python #Automation #AIAgents #AIops

🚨 BREAKING: A Go developer on Github built a full AI agent that runs on a $5 VPS. It's called Picobot and it fits in a single 12MB binary. - 11 built-in tools (files, shell, web, memory, subagents) - Persistent memory across sessions - Telegram integration out of the box - Runs on 256MB RAM. Zero dependencies. 100% Opensource. MIT License. Link: https://lnkd.in/dhbjNb2p AI jobs: https://lnkd.in/dFd6gfPw

 Specific, largely verified details — the local-first architecture, 25+ connectors, approval-gating, Ollama support, MIT license, and the v0.1/Windows-signing caveats are all accurate and add genuine substance.

Every single bullet point is a specific, verifiable feature that matches the project's actual documentation — no vague claims, no unsupported superlatives, no hype language. This is about as close to a pure feature list as LinkedIn content gets.

Precise, verifiable specifics throughout — exact model size, VRAM footprint, tok/s figure, GPU target, supported clients, license, and a genuinely detailed technical explanation of why the speedup happened (matrix-vector vs matrix-matrix kernel mismatch). This is unusually rigorous, engineering-first content.

Specific, concrete technical details — exact hour count, clip count, sample rate, named languages, the enhancement pipeline steps (vocal isolation → de-reverb → denoise), and working code to load it. This is genuinely useful, actionable content if the numbers hold up.

The tool, setup steps, and 200M+ patent database figure are accurate and verifiable — this is a genuinely useful, correctly described workflow using a real MCP server.

Dive into LLMs like ChatGPT (by Andrej Karpathy) This video tutorial covers - pretraining data (internet) - tokenization - neural network I/O - neural network internals - inference - training and inference - Llama 3.1 base model inference - pretraining to post-training - post-training data (conversations) - hallucinations, tool use, knowledge/working memory - knowledge of self - models need tokens to think - tokenization revisited: models struggle with spelling - jagged intelligence - supervised finetuning to reinforcement learning - reinforcement learning - DeepSeek-R1 - AlphaGo - reinforcement learning from human feedback (RLHF) - keeping track of LLMs - where to find LLMs Video lecture - https://lnkd.in/gV34nrXW

 Extremely dense, verifiable specifics — exact release date, model names, precise speed multipliers, VRAM savings, and a correctly sourced, non-obvious license distinction that most people writing about this repo would miss entirely.

🚨 Moonshot AI just open-sourced a serious Claude Code competitor. It’s called Kimi Code CLI — an AI coding agent that runs directly inside your terminal. And this isn’t just another Claude Code wrapper. Kimi Code CLI can: 🎥 Accept screen recordings and demo videos as input 🧠 Run separate coder, planning and exploration agents in isolated contexts 📝 Create a plan before making changes to your files 🔌 Configure MCP servers conversationally with "/mcp-config" ⚡ Start in milliseconds with no Node.js setup 💻 Work with VS Code, JetBrains, Zed and other ACP-compatible editors 🌐 Search documentation and browse the web 🛠️ Read files, edit code and execute terminal commands The CLI is open source and free to install. You can connect it to Moonshot’s Kimi models or configure another compatible model provider. Kimi K3, Moonshot’s latest flagship model, also supports: → A context window of up to 1 million tokens → Native visual understanding → Long-horizon coding and agentic workflows → Large codebase analysis Claude Code, Codex, Cursor and OpenCode now have another serious competitor. Would you trust an open-source coding agent with your production codebase?

𝗟𝗲𝗮𝗿𝗻 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁 𝗙𝗢𝗥 𝗙𝗥𝗘𝗘AI agents can plan, reason, use tools, access memory, and take actions on your behalf. They're quickly becoming the foundation of modern AI products.To make this easier to understand, I built an open-source repository that covers the core concepts behind AI agents in a simple and practical way.𝗚𝗶𝘁𝗵𝘂𝗯 𝗿𝗲𝗽𝗼 𝗵𝗲𝗿𝗲: https://lnkd.in/g7BmXKVr𝗜𝗻𝘀𝗶𝗱𝗲 𝘆𝗼𝘂'𝗹𝗹 𝗳𝗶𝗻𝗱:• Agent architectures• Memory systems• Planning and reasoning techniques• Multi-agent workflows• Tool calling and function execution• RAG-powered agents• Frameworks and implementation examplesWhether you're a developer, founder, or AI enthusiast, this repo can help you move from "using AI" to actually building with it.

No amount of sophisticated retrieval can fix a misunderstood query. Bad query in → wrong context back → your agent fails downstream. 𝘎𝘢𝘳𝘣𝘢𝘨𝘦 𝘪𝘯, 𝘨𝘢𝘳𝘣𝘢𝘨𝘦 𝘰𝘶𝘵. This is why 𝗾𝘂𝗲𝗿𝘆 𝗮𝘂𝗴𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 is the first step in any good agentic RAG pipeline. And in my new video, it's part one of the five steps of context engineering: query augmentation, retrieval, memory, tools, and agents. User messages are almost never pipeline-ready. People are vague, use shorthand, ask multi-part questions without realizing it, misspell things. Query augmentation translates messy human input into something the system can actually use. Three traditional techniques: 1️⃣ 𝗥𝗲𝘄𝗿𝗶𝘁𝗶𝗻𝗴 - clarifying the query into cleaner terms so retrieval can match it against relevant documents. 2️⃣ 𝗘𝘅𝗽𝗮𝗻𝘀𝗶𝗼𝗻 - generating related terms and phrasings that capture what the user probably meant, even if they didn't write it exactly. 3️⃣ 𝗗𝗲𝗰𝗼𝗺𝗽𝗼𝘀𝗶𝘁𝗶𝗼𝗻 - breaking a complex multi-part question into focused sub-questions that can be answered independently then synthesized. A 𝗾𝘂𝗲𝗿𝘆 𝗮𝗴𝗲𝗻𝘁 can use all three of those techniques dynamically, instead of it being a fixed step. So instead of hardcoded augmentation rules, an LLM reasons about user intent and dynamically decides how to query the data. We built this into Weaviate's Query Agent, which: • Takes a natural language question • Understands the structure of your data collections • Decides which collections are relevant • Constructs the right query, including filters and aggregations • Evaluates whether results actually answer the question • If not, reformulates and tries again • Handles follow-ups by keeping awareness of conversation context This handles messy, incomplete, real-world queries in ways fixed methods simply can't. Every layer downstream - retrieval, memory, tools, agent reasoning - depends on getting this layer right first. Full video covering all five layers of context engineering 👇 https://lnkd.in/eWbWB3D5

Dense with accurate, specific technical detail — exact header names, the three broken/rebuilt features, the handle-passing pattern, and correct architectural reasoning throughout. The restaurant/waiter analogy is a genuinely useful explanatory device, not filler.

Claude Fable 5 just solved a problem I couldn't crack with Opus.A client runs a small logistics company. 40 employees. Invoices come in as scanned PDFs, photos from drivers, handwritten notes.Before any automation, their bookkeeper was spending 30+ hours a week on data entry.We built workflow automations. Cut it down. But even with the latest models, they still needed 15+ hours of manual verification. The handwritten stuff broke it. Photos with bad lighting broke it. Multi-page invoices with inconsistent formatting broke it.Fable 5 handled all of it.The vision capabilities are a tier above. It reads charts, reconstructs data from screenshots, parses handwriting that made Opus hallucinate.Anthropic released it yesterday. First Mythos-class model available to the public. SWE-Bench Pro jumped from 69.2% on Opus 4.8 to 80.3% on Fable 5.The context window is 1 million tokens.Yes, the pricing is steep. $10 per million input tokens, $50 per million output. Double what Opus costs.But for this client, the math worked. 15 hours of bookkeeper time per week versus a few hundred dollars in API calls that actually produce clean data.Small businesses don't need cheaper models. They need models that work on the messy real-world inputs they actually have.Fable 5 is the first one I've seen do that reliably.Video credit: Hyperagent

𝗠𝗼𝘀𝘁 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝗶𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝘀 𝗱𝗼𝗻'𝘁 𝗿𝗲𝗷𝗲𝗰𝘁 𝘆𝗼𝘂 𝗯𝗲𝗰𝗮𝘂𝘀𝗲 𝘆𝗼𝘂 𝗰𝗮𝗻'𝘁 𝗯𝘂𝗶𝗹𝗱 𝗮𝗻 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁. 𝗧𝗵𝗲𝘆 𝗿𝗲𝗷𝗲𝗰𝘁 𝘆𝗼𝘂 𝗯𝗲𝗰𝗮𝘂𝘀𝗲 𝘆𝗼𝘂 𝗰𝗮𝗻'𝘁 𝗲𝘅𝗽𝗹𝗮𝗶𝗻 𝗵𝗼𝘄 𝗶𝘁 𝘁𝗵𝗶𝗻𝗸𝘀. Everyone is building AI agents. Very few understand how they actually make decisions. Interviewers aren't impressed because you used LangChain or LangGraph. They're interested in whether you understand how an agent reasons, when it should use tools, when planning is better than ReAct, how reflection improves output quality, and why different agent patterns exist. That's why Agentic AI interviews have shifted from prompt engineering to agent architecture, reasoning patterns, and decision-making. That difference is not prompting. It is agent reasoning. If you understand these concepts, you'll be able to answer unfamiliar interview questions with confidence instead of memorizing frameworks. This guide covers some of the most important Agentic AI design patterns, including: ✔ Sequential, Tool Use & ReAct Patterns ✔ Planning & Reflection Workflows ✔ When to Use Each Pattern ✔ Trade-offs & Best Practices ✔ Real Agent Design Examples These aren't just interview concepts. They're the same patterns used to build production AI assistants, autonomous workflows, coding agents, research agents, and enterprise AI systems. — 💙 𝗦𝗮𝘃𝗲 𝘁𝗵𝗶𝘀 𝗯𝗲𝗳𝗼𝗿𝗲 𝘆𝗼𝘂𝗿 𝗻𝗲𝘅𝘁 𝗔𝗜 𝗶𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄. If you're planning to move beyond theory and actually build these patterns in production, I'd also recommend checking out SmartSkale 𝗹𝗶𝘃𝗲 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝗪𝗼𝗿𝗸𝘀𝗵𝗼𝗽. You'll learn 𝗟𝗮𝗻𝗴𝗚𝗿𝗮𝗽𝗵, 𝗗𝗲𝗲𝗽 𝗔𝗴𝗲𝗻𝘁𝘀, 𝗠𝗖𝗣, 𝗠𝗲𝗺𝗼𝗿𝘆, 𝗧𝗼𝗼𝗹 𝗖𝗮𝗹𝗹𝗶𝗻𝗴, 𝗮𝗻𝗱 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻-𝗿𝗲𝗮𝗱𝘆 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 while building, debugging, and deploying a real AI agent through live, hands-on implementation. 📅 𝟮𝟲 𝗝𝘂𝗹𝘆 𝟮𝟬𝟮𝟲 (𝗦𝘂𝗻𝗱𝗮𝘆) ⏰ 𝟭𝟬:𝟬𝟬 𝗔𝗠 𝗜𝗦𝗧 🎁 𝗨𝘀𝗲 𝗰𝗼𝗱𝗲: 𝗦𝗔𝗩𝗘𝟭𝟬 𝗳𝗼𝗿 𝗮𝗻 𝗮𝗱𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 𝟭𝟬% 𝗱𝗶𝘀𝗰𝗼𝘂𝗻𝘁. 𝗪𝗼𝗿𝗸𝘀𝗵𝗼𝗽 :- https://lnkd.in/gaU3ieaw 𝗙𝗼𝗹𝗹𝗼𝘄 𝗞𝗮𝗺𝗮𝗹 𝗦𝗵𝗮𝗿𝗺𝗮 𝗳𝗼𝗿 𝗺𝗼𝗿𝗲 𝗔𝗜, 𝗣𝘆𝘁𝗵𝗼𝗻, 𝗟𝗟𝗠𝘀, 𝗦𝘆𝘀𝘁𝗲𝗺 𝗗𝗲𝘀𝗶𝗴𝗻, 𝗦𝗤𝗟, 𝗗𝗦𝗔, 𝗮𝗻𝗱 𝗦𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗰𝗼𝗻𝘁𝗲𝗻𝘁. #AgenticAI #GenerativeAI #LangGraph #LLMs #AIEngineering

Obsidian is usually placed in the same category as Notion, Apple Notes, Roam, or any other app where people collect thoughts. That comparison is useful at the interface level, but it hides the more important design choice. Obsidian’s central object is not a workspace hosted inside the product. It is a folder of Markdown files on your own machine. The app sits on top of that folder and gives you ways to inspect, connect, search, visualize, and extend those files. That is a very different architecture from the productivity tools most people are used to. In many modern tools, the database is the source of truth and the interface is the only practical way to reach it. In Obsidian, the file remains the source of truth. A note can be read outside the app. A vault can be backed up like any other folder. Links are written into the text. Metadata can live inside the file. The useful thing is not that Obsidian has a graph view or a plugin marketplace. The useful thing is that it keeps the durable layer simple enough to survive the interface. This also explains why Obsidian becomes more interesting in the AI era. LLMs work best when the material they operate on is explicit, inspectable, and easy to transform. A folder of Markdown notes is a much better substrate for that than an opaque productivity database. A model can summarize notes, extract metadata, suggest links, generate index pages, or turn raw research into a more coherent local wiki. But the human still needs to audit the result, because a knowledge base that looks organized can still be wrong. The point is not to automate thinking. The point is to make the maintenance of knowledge less fragile.

Flat vector search is why your RAG agent keeps getting context wrong!Traditional RAG does a flat vector lookup and returns isolated snippets. No hierarchy, no relationships, no way to navigate the document the way a human reader would. Your agent gets fragments instead of context, and the answers suffer for it.Knowhere is the memory layer between your documents and your AI agents. It ingests unstructured documents and builds persistent, navigable memory: parsing, hierarchy extraction, multi-modal structuring, and graph construction in a single pipeline. Every chunk retains full semantic context.How it works:• Parse routes PDFs, Office files, images, tables, Markdown, and text to specialized parsers• Structure uses a proprietary Tree-like algorithm to reconstruct the full document hierarchy instead of flattening it into a sequence• Build Memory stores chunks, navigation trees, summaries, and graph links as agent-ready context• Agentic Retrieval fuses keyword, path, content, and semantic signals, then walks section trees and graph links to drill into the most relevant regionsThe performance gap is real. Agents using Knowhere hit +36% first-try accuracy and +11% recall over agents working from raw documents, with 79% accuracy with feedback versus a 53% ceiling on raw docs.

China has killed the GPU mafia 🤯 They open-sourced a framework that runs DeepSeek-R1 (671B params) on a single 24GB graphics card. → 3-28x faster inference → Fine-tune DeepSeek-V3 on 4x 4090s (~80GB) → 6-12x faster than ZeRO-Offload → Runs Kimi-K2, GLM, Qwen3-Next out of the box 17.3k stars. 100% Open Source. https://lnkd.in/dD5DwJ_s

Specific, verifiable numbers throughout — exact file sizes (1.56TB → 594GB), the 62% reduction, the 78.9% accuracy figure, named tools (Unsloth Studio, llama.cpp), and a working guide/GGUF link. This is genuinely substantive technical content.

Garry Tan’s GStack has gone completely viral, and for good reason.The YC CEO open-sourced his personal toolkit, and it's the ultimate cheat code for devs.It turns Claude Code from a basic chatbot into an entire virtual engineering department.Instead of basic prompts, you summon specialized AI "brains" on demand.Tens of thousands are already using it.→ The Visionary: /plan-ceo-review acts like Brian Chesky. It stops you from building boring features and pushes for magic.→ The Architect:/plan-eng-review draws sequence diagrams and state machines before coding.→ The Paranoid Reviewer: /review looks for N+1 queries and stale reads.→ The QA Lead:/qa literally logs into your staging environment, clicks around, takes screenshots, and gives your app a health score in 60 seconds.The QA tool alone is built on Bun and Playwright, running 20x faster than Claude’s native Chrome MCP with zero context bloat.He used this exact setup to ship 100 PRs a week for the last 50 days.100% free and open-source.Here's the repo → https://lnkd.in/eX9sge3Q