Claude Code just got the one thing it was missing: memory Every AI coding session has the same tax. You explain the codebase again. You re tell the decisions. You paste the same context until the window breaks. Claude Mem flips that. It runs as a Claude Code plugin that quietly records what happened in your session, summarizes it, and brings back only the relevant pieces next time. The repo says it captures tool use and injects context across sessions, with a layered search flow designed to cut token waste. The result is what people wanted all along: you can stop resetting the assistant every morning. The speed of adoption is the signal. It surged past 46k GitHub stars in about 48 hours and is now around 49.5k stars on GitHub. One command install. Local storage. Pick up where you left off. If you use Claude Code daily, this is worth testing just to feel what continuity does to your workflow. 👉 https://lnkd.in/g76xUFqD #ClaudeCode #AIcoding #DeveloperTools #OpenSource #Productivity #LLM #AgenticAI

Stop talking about how wonderful AI is as a generic concept and talk about what you're actually doing with it instead. I'll go first. I just used Codex and Claude to move 3 of my related patch-pulse open source projects to a monorepo. I used T3Code to do a bunch of focused tasks, like: - migrate eslint and prettier to oxlint and oxfmt respectively - move from npm to pnpm - add knip to the repo and remove dead code - identify shared logic between the repos and move to a shared package I was wasting $7 a month for the past 2 years on a Render service to host and hourly cron job. I used my LLMs to migrate from Supabase & Render to Convex since I already have a Convex subscription to save that money. AI didn't do something for me that would have taken me days in just an hour or two. It did something for me that I wouldn't have bothered doing at all if I didn't have it. Use LLMs to do the tedious crap for you. #ai #claude #cursor #codex #convex

Playing around with agents is so much fun! During my (very little) spare time I got Claude to re-write the whole agent client framework for Tinker. One thing I learned more from this re-writing is that agents just don't magicallly invoke the tools (you know, the code for it to interact with the files). The LLM just responds with something like "I want to use tool X for this task" and I have to route the tool X code to it. What seems to be more interesting is that the newer versions of Claude like Opus 4.7 seems to be much eager to use the bash tool. And for Tinker I assign each conversation to a SQLite database because.... it's lightweight I suppose? I was thinking like: What if the agents can just exchange conversations? What if I can summarize the whole conversation, put it in a .db file, and refer to it in another SQLite database? The features are many but the time is just so little. For the next steps: I really want to build a nice sandbox environment, then clone Tinker's source code there, then the agent can finally build itself (with restrictions). Finally, a self-driving code base! And maybe moving the entire agent platform to Kubernetes, you know, for fun? :) There are just so many possibilities. P/S: The Discord client with the thread feature is really nice as well. #agent #stripeminions #rampinspect #ai #llm #coding

🚀 𝗜 𝗯𝘂𝗶𝗹𝘁 𝘁𝗵𝗲 𝗰𝗼𝗺𝗽𝗹𝗲𝘁𝗲 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝗥𝗼𝗮𝗱𝗺𝗮𝗽 2026 — 𝗳𝗿𝗼𝗺 𝘇𝗲𝗿𝗼 𝘁𝗼 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻-𝗿𝗲𝗮𝗱𝘆 𝗮𝗴𝗲𝗻𝘁𝘀. Most people jump straight into LangChain or AutoGPT without understanding what's actually happening under the hood. That's why I mapped out the few-step journey I wish I had when I started. Here's the path that actually works: 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻 𝗙𝗶𝗿𝘀𝘁 → Python + APIs — because agents are just code → LLM Basics — understand tokens, context, limitations → Prompting + Tool Use — move beyond chat, into action 𝗕𝘂𝗶𝗹𝗱 𝘁𝗵𝗲 𝗠𝗲𝗺𝗼𝗿𝘆 → Embeddings + Retrieval — make agents use YOUR data → RAG Systems — the core pattern behind 80% of real agents 𝗕𝘂𝗶𝗹𝗱 𝘁𝗵𝗲 𝗔𝗴𝗲𝗻𝘁 → Agent Frameworks — LangChain, LlamaIndex, OpenAI Assistants → Multi-Agent Systems — when one agent isn't enough → Planning + Reasoning — make agents think before they act 𝗠𝗮𝗸𝗲 𝗶𝘁 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻-𝗥𝗲𝗮𝗱𝘆 → Evaluation + Guardrails — because hallucinations are real → Deployment + Scaling — FastAPI, async, monitoring → Build Real Agent Systems — research agents, coding assistants, automation workflows The secret? Build at every single step. Don't just read — ship. Follow Alpna P. for more content! Connect me on Instagram: https://lnkd.in/gTQhjM_5 #AgenticAI #LLM #MachineLearning #AI #ArtificialIntelligence #codewithalpana

158K stars in 2 days? I had to look twice. So I dug in. Here's what actually happened 👇 On March 31 at 4 AM, Anthropic accidentally leaked Claude Code's entire source code. One missing file. 512,000 lines of proprietary AI — exposed. That same morning, a developer named Sigrid Jin stayed up and rebuilt the whole thing from scratch. No stolen code. Just architecture, memory, and one very long night. He called it Claw Code. And the dev world lost it. Why it blew up: → Developers had wanted a free alternative to Claude Code for a long time → This wasn't a hobby project — it was production-ready from day one → Works with any AI provider, not just Anthropic → Runs fully local, no subscription, no cloud lock-in → Open source means you own it completely What it actually does: You connect it to your AI provider of choice, give it an instruction, and it gets to work — writing code, editing files, running tasks — all from your own machine. How to set it up (5 minutes, no tech skills needed): 1. Visit https://lnkd.in/dvsPwZsp and download the ZIP 2. Unzip it and open the folder 3. Open the README — it walks you through everything in plain English 4. Paste in your API key when asked 5. Launch it and type your first instruction That's genuinely it. One person. One night. Something 100,000+ engineers were waiting for. Have you tried Claw Code? #AI #OpenSource #GitHub #DeveloperTools #AIAgents #ArtificialIntelligence #MachineLearning #SoftwareEngineering #Coding #Tech #Innovation #OpenSourceAI #AITools #Developers #FutureOfWork

Claude Code: Leaked, De-obfuscated, and... has a pet? 🐶 Is it an April Fool’s prank? Or the most high-profile "oops" in AI history? 😅 Yesterday, a routine update to Claude Code (v2.1.88) accidentally included a 60MB source map file. For those not in the dev weeds: that’s basically handing the world a "de-obfuscator" for the entire codebase. Within hours, 512,000 lines of TypeScript were mirrored across GitHub. While Anthropic has confirmed it was human error (not a breach), the internet has already spent the night "code-spelunking." The wildest finds so far: 🐶 Project BUDDY: An unreleased "Tamagotchi-style" AI pet that lives in your terminal. It has species like "axolotl" and stats for CHAOS and SNARK. (Finally, a reason to keep my terminal open!) 🌙 Dream Mode (KAIROS): A feature where Claude "dreams" at night to consolidate memories of your codebase while you sleep. 🤫 Undercover Mode: Ironically, a feature built to prevent Claude from leaking internal secrets. The Takeaway : A 60MB mistake is a massive reminder that even in the age of AGI, the fundamentals still matter. Whether you're building a world-class LLM or a weekend project, your .npmignore is the last line of defense for your IP. Stay sharp, devs. #Anthropic #ClaudeCode #SoftwareEngineering #AI #TechNews #AprilFools #WebDev #Sourcecode

I got tired of burning $5 on a single Claude Code session.  So I built a plugin that cuts it in half. Automatically.  Pith hooks into Claude Code's lifecycle and removes what you don't need before it hits context:  → File reads return imports + signatures only (−88%)  → Bash output becomes errors + 3-line summary (−91%)  → Grep caps at 25 matches  → Large outputs offloaded to disk, pointer left in context  This session alone: 113k tokens saved. $1.39 back. $44.89 lifetime.  One install. Works in every project, forever.  Open source → github.com/abhisekjha/pith  #ClaudeCode #AI #DeveloperTools #OpenSource Anthropic

Just open-sourced santhoshstack, my personal Claude Code skills. Fork it. Use it. Improve it. Three skills built from 4 weeks of my mad claude code exploration: → context-manager (useful for people dont know structured documentation) Your context travels with the codebase, not the chat. Works across Claude, Codex, Antigravity, and any AI tool.  This skill builds a docs/context/ folder inside your repo. Every tool reads it. A pointers.md index tells the AI exactly what context to load for the current task → frugal-token-usage (useful for people with less token limit) Stops token waste mid-session. Heavy commands, full file reads, verbose output. It even hands off terminal commands to you instead of running them itself. I started with these as personal rules in my own config. (I tried building this skill without affecting the output quality) → foresight-intelligence (bonus) A think tank skill inspired by the Institute for the Future. Give it any question about the future, and it surfaces structural drivers, historical analogues, and four distinct scenarios with a single decisive recommendation. Not a toy. A structured thinking tool. Of course, I got inspiration from Garry Tan Gstack. #claudecode #claudeskills

OpenAI's Codex just hit 3 million weekly active developers. Claude Code's fingerprint is on 4% of all GitHub commits. Cursor hasn't published usage numbers in months. The AI coding wars have a winner emerging — and it's not the one with the most features. I wrote a full breakdown covering: → Why these three tools are not actually competitors (they're three different paradigms) → The benchmark that matters — not speed, but rework required after the AI is done → The pricing reality that pricing pages won't tell you → Why this is less a tools story and more a labour market story The number that stopped me: Claude Code is the #1 most-loved developer tool in a survey of 906 professional engineers. 46%. That kind of loyalty doesn't come from features. It comes from trust. Meanwhile Codex grew 6x inside enterprise accounts between January and April 2026. Two very different growth signals. Both meaningful. Full breakdown 👇(in comments) #AITools #ClaudeCode #OpenAI #DeveloperTools #FutureOfWork #AICoding #Cursor

Anthropic just had a rough morning. 🙀 A misconfigured npm package accidentally exposed Claude Code's entire source code—512,000 lines across ~1,900 files. Within hours, it hit 25,000+ stars on GitHub. But here's what's actually important: what the leak revealed about AI agent architecture. Claude Code isn't just "Claude in a terminal." It's an operating system for software work. And the memory system? It's a masterclass in how NOT to build agent memory. Here's the three-layer architecture everyone should steal: 🔹 Layer 1: A tiny always-loaded index (~150 chars per entry, just pointers) 🔹 Layer 2: On-demand topic files fetched only when relevant 🔹 Layer 3: Raw transcripts accessed only via search, never loaded wholesale The key insight? If a fact is derivable from your codebase (debug logs, PR history, file structure), don't store it. Stale memory is worse than no memory. Claude Code even runs a background "autoDream" process that consolidates memory while it sleeps. Your agents should do the same. The irony is thick: Anthropic markets Claude Code with a strong security story, then a packaging mistake exposes the internals. But the bigger irony? OpenAI has been open-sourcing Codex on purpose. Anthropic kept the harness closed, implying the orchestration layer itself is the crown jewels. Now that layer is in daylight anyway—and the whole industry gets to learn from Claude Code's best practices. The AI coding race quietly shifted from "who has the smartest model?" to "who has the best harness?" What's your take—does the leak change how you think about building agents? #AI #AgentArchitecture #Claude #SoftwareDevelopment #AITools #MachineLearning #OpenSource I bring to you all these AI news everyday through my company, you may consider support us by following Kodeeng Co. ( www.kodeengco.com )