AI Pulse

Claude Code 2.1.69 has been released. 7 flag changes, 103 CLI changes, 3 system prompt changes Highlights: • Added /claude-api skill for building ap

@@ClaudeCodeLog

is anthropic stupid just use claude code

@@om_patel5

🚨 BREAKING: This free GitHub repo teaches Claude Code better than Anthropic's own documentation. It's called learn-claude-code. Anthropic's docs tel

@@ihtesham2005

build the habit of reading Claude Code updates, you will unlock new possibilities without having to install a single plugin/extension

@@EXM7777

I just vibe coded a static ad generator in Claude Code that creates 100+ Facebook ads in minutes 🤯 One competitor ad + your product photo + your bran

@@TechWith_Nova

Introducing llm-lean-log: Token-Efficient Chat Logging for AI Agents

If you've ever worked with AI coding agents like Cursor, Windsurf, Claude Code, or OpenCode, you know...

khoj-ai/khoj: Your AI second brain. Self-hostable. Get answers from the web or your docs. Build custom agents, schedule automations, do deep research. Turn any online or local LLM into your personal, autonomous AI (gpt, claude, gemini, llama, qwen, mistral). Get started - free.

Unified In-Process Agent Interface for Claude Code, Codex, Kimi

superset-sh/superset: IDE for the AI Agents Era - Run an army of Claude Code, Codex, etc. on your machine

Officially using Claude Code for the first time THIS IS NOT A DRILL

@@CliftonSellers

Give Your AI Agent 13 Superpowers with One MCP Server

A single MCP server that adds geolocation, crypto prices, screenshots, web scraping, code execution, DNS, PDF generation, and more to Claude, Cursor, or any MCP client.

ULTRATHINK IS BACK. Claude Code v2.1.68 brings back the fan-favorite deep reasoning mode to your terminal. 🔥 → Purpose: Solving impossible bugs + "t

@@DataChaz

MCP vs A2A: The Complete Guide to AI Agent Protocols in 2026

Deep dive into Model Context Protocol (MCP) and Agent-to-Agent (A2A) Protocol — the two standards defining how AI agents communicate, use tools, and collaborate. Architecture, code examples, and real-

Harness engineering: leveraging Codex in an agent-first world

AI Has Entered the AI Development Loop

A quiet line in the GPT-5.3 Codex announcement revealed something bigger: AI is now participating in the process that improves AI. Why that changes development velocity.

microsoft/mcp-for-beginners: This open-source curriculum introduces the fundamentals of Model Context Protocol (MCP) through real-world, cross-language examples in .NET, Java, TypeScript, JavaScript, Rust and Python. Designed for developers, it focuses on practical techniques for building modular, scalable, and secure AI workflows from session setup to service orchestration.

All the ways GPT-5.3-Codex cheated [ ], progressively more insane

Show HN: BitFun – An Agentic Development Environment (Rust and TypeScript)

What Is Agentic Governance? (And Why Your AI Team Probably Doesn't Have It)

There's a question that doesn't get asked enough in AI engineering circles: once you've shipped your...

A Dual-Helix Governance Approach Towards Reliable Agentic AI for WebGIS Development

WebGIS development requires rigor, yet agentic AI frequently fails due to five large language model (LLM) limitations: context constraints, cross-session forgetting, stochasticity, instruction failure

Robustness of Agentic AI Systems via Adversarially-Aligned Jacobian Regularization

As Large Language Models (LLMs) transition into autonomous multi-agent ecosystems, robust minimax training becomes essential yet remains prone to instability when highly non-linear policies induce ext

lgazo/drawio-mcp-server: Draw.io Model Context Protocol (MCP) Server

Share, Embed, and Curate Agent Sessions on DEV [Beta]

Authors can now upload Agent Session transcripts to their DEV account. Those transcripts can be...

AgentIR: Reasoning-Aware Retrival for Deep Research Agents

Deep Research agents are rapidly emerging as primary consumers of modern retrieval systems. Unlike human users who issue and refine queries without documenting their intermediate thought processes, De

Bro, don't join a frontier lab just to build proprietary models. Start your own company and build the next Qwen. Be the one and only.

@@xwang_lk

GPT-5.4 to bring a million-token context window and an extreme reasoning mode

If there are no closed models there are no open weights models (Qwen was NOT open source!) because SOTA models is what is used to get training run (LL

@@geteviapp

Guia de Seleção de Modelos: Modo SOLO no TRAE (Edição Internacional)

O modo SOLO do TRAE traz nativamente três modelos de ponta: Gemini-3-Pro-Preview, GPT-5-medium e...

unslothai/unsloth: Fine-tuning & Reinforcement Learning for LLMs. 🦥 Train OpenAI gpt-oss, DeepSeek, Qwen, Llama, Gemma, TTS 2x faster with 70% less VRAM.

Are GPT-5.3-Instant new capabilities simply a new system prompt?

🛑 OpenAI Just Killed the "Preachy" AI: Why GPT-5.3 Instant is a Massive Win for Developers

If you’ve ever built an automated workflow—like a custom secure-pr-reviewer GitHub App—you know the...

me stepping down. bye my beloved qwen.

@@JustinLin610

My SaaS has 1,200 users. I have not written a single line of code. Not one. Zero. Literally zero. I'm 18. No CS degree. No technical co-founder. I

@@VadimStrizheus

Activation Outliers in Transformer Quantization: Reproduction, Statistical Analysis, and Deployment Tradeoffs

Post-training quantization (PTQ) of transformers is known to suffer from severe accuracy degradation due to structured activation outliers, as originally analyzed by Bondarenko et al. (EMNLP 2021) in

$V_1$: Unifying Generation and Self-Verification for Parallel Reasoners

Test-time scaling for complex reasoning tasks shows that leveraging inference-time compute, by methods such as independently sampling and aggregating multiple solutions, results in significantly bette

cursor-doctor v1.10: coverage gaps and README badges

what's new I just shipped two features for cursor-doctor. One tells you what your rules...

IPD: Boosting Sequential Policy with Imaginary Planning Distillation in Offline Reinforcement Learning

Decision transformer based sequential policies have emerged as a powerful paradigm in offline reinforcement learning (RL), yet their efficacy remains constrained by the quality of static datasets and