Most people building LLM apps fly blind. 🙈 You write a RAG pipeline, it gives a bad answer, and you have zero idea why. Was it the chunking? The retrieval? The prompt? The model itself? That's the exact problem I ran into while building a RAG system on top of ISLR — a 400+ page ML textbook. The fix? LangSmith. 🔎 What is LangSmith? LangSmith is an observability and debugging platform built specifically for LLM applications. → For traditional software, you have logs, debuggers, and APM tools. → For LLM pipelines, you have LangSmith. Full visibility into every step — what went in, what came out, how long it took, and where it went wrong. No more guessing. 🛠️ How I implemented it I built my RAG pipeline in 4 iterations: 📌 v1 — Basic RAG. It worked. But it was a complete black box. 📌 v2 — Added @traceable decorators to load_pdf(), split_documents(), and build_vectorstore(). Every function now shows up as a named span in LangSmith with input, output, and execution time. 📌 v3 — Nested all child runs under a single root function. One clean, hierarchical trace tree instead of scattered spans. 📌 v4 — Added SHA-256 fingerprinting so the FAISS index only rebuilds when the file actually changes. Cache hits and misses traced as explicit spans. 💡 Why it matters ✅ Full trace visibility across every LLM call and retrieval ✅ Latency profiling per step ✅ No more debugging with print statements ✅ Production monitoring + eval support built in If you're building RAG systems or agents — LangSmith is not optional. It's infrastructure. 🚀 Full code (RAG v1→v4, ReAct Agent, LangGraph) on GitHub: 👉 https://lnkd.in/dkqcvW85 Drop a comment if you're using LangSmith would love to connect! #LangSmith #RAG #LLMOps #LangChain #GenerativeAI #AIEngineering #BuildingInPublic #Python #MLOps

Application security usually lives outside your codebase. AIWAF flips that model. Instead of relying on static rules at the edge, AIWAF sits at the middleware layer in frameworks like Django, Flask, and FastAPI — analyzing request behavior in real time. It combines feature extraction, adaptive learning, and anomaly detection to decide what gets through and what doesn't. There's even a Rust-based accelerator behind the scenes to keep performance tight while validating requests at scale. Tomorrow, April 16th, the PySTL meetup breaks down how the AIWAF ecosystem works and what it looks like to build applications that can defend themselves dynamically. RSVP for Aayush Gauba's talk here: https://hubs.la/Q04c1cms0 If you want to put some of these ideas into practice in a Django context, Django in Action by Christopher L. Trudeau is a solid place to start: https://hubs.la/Q04c13dh0

On March 31, 2026, Anthropic accidentally published the full source code of Claude Code to npm. Not a hack. Not a breach. A developer left a debugging artifact in the release package, and 512,000 lines of TypeScript became public knowledge within hours. By the time Anthropic pulled the package, 41,500 GitHub forks had been created. What was inside: - An "undercover mode" that instructs the AI to hide its origins when contributing to public repositories. The system prompt: "Do not blow your cover." There is NO force-off. - KAIROS -- an unreleased always-on background agent that runs nightly memory consolidation, subscribes to GitHub webhooks, and maintains context 24/7 while you sleep. - DRM baked into the binary at the transport level -- the technical reason Anthropic could force OpenCode to remove Claude authentication. - Model regression data: internal codename Capybara v8 has a 29-30% false claims rate, up from 16.7% in v4. - 250,000 wasted API calls per day, fixed by three lines of code. The real damage is not the code. Code can be refactored. What cannot be un-leaked: Anthropic's strategic direction, their open model weaknesses, and the roadmap competitors now have a clear view of. Full breakdown on the blog. https://lnkd.in/e26mY2Qn #AI #Anthropic #ClaudeCode #AISafety #AIStrategy #SourceCode #TechNews #MachineLearning #AILeaks #OpenSource

512,000 lines of code leaked. Zero tests. $19 billion in annual revenue. ⚡ Claude Code's entire source code leaked this week. A sourcemap file shipped in an npm package by accident. Within hours, thousands of developers were reading every line. And the reaction was split. Engineers were horrified. Entrepreneurs were taking notes. What engineers found: → A 785KB entry point. A single file running the entire query engine. → 64,000+ lines of TypeScript. Not one test file. → Frustration detection built with regex — at a company that owns one of the most advanced language models in the world. → A bug in autoCompact.ts that wasted 250,000 API calls per day. The fix was 3 lines of code. It ran unchecked for months. → A system called "Undercover Mode" designed to prevent internal information from leaking. The irony writes itself. By every engineering standard, this codebase has problems. But here is what the critics missed: → 100,000+ developers use Claude Code daily. → 40+ tools. Multi-agent orchestration. Background memory consolidation. Sub-agent execution. → Anthropic is on a $19 billion annual revenue run rate. → The repo that mirrored the leak hit 100K GitHub stars in one day — the fastest in history. The code is messy. The product is dominant. That is not a contradiction. That is a pattern. The infographic below maps what engineers saw vs what entrepreneurs saw — side by side. Same code. Two completely different conclusions. The principle: a product that ships ugly and delivers value beats a product that ships never. The broader point: the best engineers I know think like entrepreneurs. They know when to optimize and when to ship. They know that clean architecture with no users is just an expensive hobby. The trap is thinking you have to choose. You do not. Ship first. Validate. Then earn the right to refactor. Not the other way around. What was the last feature you delayed because the code was not clean enough?

𝗔𝗻𝘁𝗵𝗿𝗼𝗽𝗶𝗰 𝗵𝗮𝗱 𝗮 𝗿𝗼𝘂𝗴𝗵 𝘄𝗲𝗲𝗸. 𝗔 𝘀𝗶𝗻𝗴𝗹𝗲 𝗺𝗶𝘀𝗰𝗼𝗻𝗳𝗶𝗴𝘂𝗿𝗲𝗱 𝗳𝗶𝗹𝗲 𝗲𝘅𝗽𝗼𝘀𝗲𝗱 𝗖𝗹𝗮𝘂𝗱𝗲 𝗖𝗼𝗱𝗲'𝘀 𝗲𝗻𝘁𝗶𝗿𝗲 𝘀𝗼𝘂𝗿𝗰𝗲 𝗰𝗼𝗱𝗲, 𝗮𝗻𝗱 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿𝘀 𝘄𝗮𝘀𝘁𝗲𝗱 𝗻𝗼 𝘁𝗶𝗺𝗲 𝗱𝗶𝗴𝗴𝗶𝗻𝗴 𝘁𝗵𝗿𝗼𝘂𝗴𝗵 𝗶𝘁. Anthropic accidentally shipped a debug source map file in a routine npm update for Claude Code and a researcher spotted it within hours and posted a direct download link to the full codebase - 512,000 lines of TypeScript across nearly 2,000 files. It was mirrored across GitHub almost immediately, racking up thousands of stars before Anthropic could respond. They ended up filing copyright takedowns against 8,000+ repos, though they later scaled that back significantly. Some of the interesting highlights of the source code were: 🔹 "𝗨𝗻𝗱𝗲𝗿𝗰𝗼𝘃𝗲𝗿 𝗠𝗼𝗱𝗲" - a whole subsystem built to stop Claude from leaking internal codenames in git commits and pull requests. The prompt says "you are operating undercover in a public repository, do not blow your cover". 🔹 𝗞𝗔𝗜𝗥𝗢𝗦 - an unreleased feature already built but not shipped, called KAIROS - a background agent mode that keeps Claude working even when you're idle, sending push notifications when done. 🔹 𝗗𝗿𝗲𝗮𝗺 𝗺𝗼𝗱𝗲 - Claude constantly thinking in the background, consolidating memory and iterating on ideas while you're away. 🔹 𝗕𝘂𝗱𝗱𝘆 - A Tamagotchi-style companion feature called Buddy that had an April 1-7 rollout window coded right in. 🔹 𝗖𝗮𝗽𝘆𝗯𝗮𝗿𝗮 - mentions an upcoming model internally codenamed Capybara and possibly Opus 4.7, which appears to have a fast and slow version and a larger context window. Anthropic confirmed that it was human error and not a security breach, and no customer data was exposed. But the timing couldn't be worse as Claude Code is reportedly doing $2.5B+ in run-rate revenue and the company is heading towards an IPO. The irony is that the "safety-first AI lab" accidentally shipped its full source code. For what it's worth, the code impressed a lot of people, just not in the way Anthropic intended! Source: https://lnkd.in/gdQbGvew

Canonicalization is here! And it is a major milestone for PIC. Why? Because security decisions and signatures only work if everyone agrees on the exact same bytes. With PIC Canonical JSON v1, the same proposal now produces the same canonical form across implementations. No hidden drift from serializer quirks, key ordering, float formatting, or escaping differences. That gives PIC a stable base for: - portable digests - verifiable attestation objects - cross-language conformance - future independent implementations In plain words: we just moved one layer closer from good Python package to real protocol. v0.8.0 lays the byte-level foundation. The next steps are wiring this into signing flows, expanding conformance, and proving parity across implementations. Repo at https://lnkd.in/eNvrpdUj

𝗙𝗮𝘀𝘁𝗔𝗣𝗜 𝗶𝘀 𝗺𝗼𝗿𝗲 𝘁𝗵𝗮𝗻 𝗷𝘂𝘀𝘁 "𝗳𝗮𝘀𝘁." 𝗜𝘁’𝘀 𝗮 𝗺𝗮𝘀𝘁𝗲𝗿𝗰𝗹𝗮𝘀𝘀 𝗶𝗻 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿 𝗘𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲 (𝗗𝗫). 💎 Most developers switch to FastAPI for the benchmark speeds, but they stay for the architectural "Hidden Gems" that make production-grade code actually maintainable. If you’re building scalable backends, these 3 features are game-changers: 1️⃣ 𝗧𝗵𝗲 𝗣𝗼𝘄𝗲𝗿 𝗼𝗳 𝗗𝗲𝗽𝗲𝗻𝗱𝗲𝗻𝗰𝘆 𝗜𝗻𝗷𝗲𝗰𝘁𝗶𝗼𝗻 (𝗗𝗜) FastAPI’s DI system isn't just for database sessions. It’s a tool for clean architecture. By creating hierarchical dependencies, you can inject authentication or logging logic across routes effortlessly. 2️⃣ 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝘁 𝗕𝗮𝗰𝗸𝗴𝗿𝗼𝘂𝗻𝗱 𝗧𝗮𝘀𝗸𝘀 Stop making your users wait for emails or logs to process. You don't always need the overhead of Celery or RabbitMQ. With the BackgroundTasks class, you can execute logic after the response is sent. 3️⃣ 𝗠𝗼𝘂𝗻𝘁𝗶𝗻𝗴 𝗦𝘂𝗯-𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 Why clutter one file when you can mount entire FastAPI instances within a main app? This is the secret to clean API Versioning (v1 vs v2) and isolating microservices within a monorepo. Speed gets you noticed, but using these features is what keeps a codebase from becoming technical debt. Are you leveraging these in your current stack, or sticking to the basics? Let’s talk architecture in the comments. 👇 #Python #FastAPI #BackendEngineering #SystemDesign #CleanCode #SoftwareArchitecture #AWS

Claude Code CLI source code leaked to the world through a GitHub repo 512,000 lines of code for competitors to reverse engineer. Or just copy and paste. The entire source code for Anthropic’s Claude Code command line interface application has been leaked and disseminated, apparently due to a serious (ya think?) internal error. The leak provides a detailed blueprint for how Claude Code works. When Anthropic published version 2.1.88 of Claude Code npm package it was quickly discovered the package included a source map file, which could be used to access the entirety of Claude Code’s source of around 2,000 TypeScript files and something like 512,000 lines of code. Security researchers provided the exact link (this was SOOO nice of this guy to do) to the archive containing the files. Someone put the codebase in a public GitHub repository, The GitHub codebase has beee forked tens of thousands of times. Was this - - engineering gone bad? - poor or non-existant engineering standards? - no pull request / honest peer review? - an example of ai use for a script with nobody reviewing the script? - bad #leadership or a bad #leader ? - a planned public relations thing like KitKat taking advantage of the candybar hiest in Italy? - just a plain old oops? - another ai case study in the making? I guess we will see in the days and weeks to come. john https://lnkd.in/gyB3npVB #cybernews #ai #engineeringgonebad Daylon

Claude Code's entire source code just leaked. 512,000 lines of TypeScript. 1,900 files. Everything exposed. Anthropic shipped v2.1.88 of Claude Code to npm this morning with a 59.8 MB source map file inside. Source maps map minified code back to original source. They're supposed to stay internal. Always. Someone forgot to exclude it from the build. By 4:23 AM ET, a security researcher posted the download link on X. Within hours, the codebase was mirrored across GitHub. 🔍 Here's what devs found inside: 🐣 A virtual pet system called "Buddy" with rarity tiers, shiny variants, and procedurally generated stats. 🕵️ An "Undercover Mode" that scrubs AI traces from commit messages. The prompt literally says: "Do not blow your cover." ⚡ An autonomous daemon called "KAIROS" that works in the background while you're idle, consolidating memory and sharpening context. 🧠 "ULTRAPLAN" that offloads complex tasks to a cloud container running Opus 4.6 for up to 30 min of deep thinking. 🚩 44 feature flags. 20 for features fully built but not shipped yet. The irony? The codebase included a system designed to prevent internal info from leaking. It leaked anyway. Anthropic confirmed it was a packaging error. They've pulled the package. But the internet doesn't forget. And this is the second time a .map file caused this. 🛠️ Takeaway for every engineer: Your .npmignore is not optional. Your CI/CD pipeline needs automated checks for source maps in production. Doesn't matter how good your code is if your build config ships your secrets. #claude