Agents &amp; Code

GitHub Copilot Alternatives Every Developer Should Try - Analytics Insight

GitHub Copilot Alternatives Every Developer Should Try Analytics Insight

Openai Codex -The AI Code Editor - About Chromebooks

Openai Codex -The AI Code Editor About Chromebooks

InfoQ AI/ML

Google Brings MCP Support to Colab, Enabling Cloud Execution for AI Agents

Google has released the open-source Colab MCP Server, enabling AI agents to directly interact with Google Colab through the Model Context Protocol (MCP). The project is designed to bridge local agent workflows with cloud-based execution, allowing developers to offload compute-intensive or potentially unsafe tasks from their own machines. By Robert Krzaczyński

ArXiv cs.AI

KD-MARL: Resource-Aware Knowledge Distillation in Multi-Agent Reinforcement Learning

Real world deployment of multi agent reinforcement learning MARL systems is fundamentally constrained by limited compute memory and inference time. While expert policies achieve high performance they rely on costly decision cycles and large scale models that are impractical for edge devices or embedded platforms.

ArXiv cs.AI

TurboAgent: An LLM-Driven Autonomous Multi-Agent Framework for Turbomachinery Aerodynamic Design

The aerodynamic design of turbomachinery is a complex and tightly coupled multi-stage process involving geometry generation, performance prediction, optimization, and high-fidelity physical validation.

ArXiv cs.AI

Qualixar OS: A Universal Operating System for AI Agent Orchestration

We present Qualixar OS, the first application-layer operating system for universal AI agent orchestration. Unlike kernel-level approaches (AIOS) or single-framework tools (AutoGen, CrewAI), Qualixar OS provides a complete runtime for heterogeneous multi-agent systems spanning 10 LLM providers, 8+ agent frameworks, and 7 transports.

Meta's new model is Muse Spark, and meta.ai chat has some interesting tools

Meta announced Muse Spark today, their first model release since Llama 4 almost exactly a year ago . It's hosted, not open weights, and the API is currently "a private API preview to select users", but you can try it out today on meta.ai (Facebook or Instagram login required). Meta's self-reported benchmarks show it competitive with Opus 4.6, Gemini 3.1 Pro, and GPT 5.4 on selected benchmarks, though notably behind on Terminal-Bench 2.0.

TechCrunch AI

Poke makes using AI agents as easy as sending a text

Poke brings AI agents to everyday users via text message by handling tasks and automations without complex setup, apps, or technical know-how.

Replit Blog

Beyond the App: Using Vibe Coding to Ship Decks, Dashboards, and Launch Assets

This is part 5 of a 6-part series we’re running about how product managers are using AI tools and vibe coding. Written by and for product managers. Summary Product managers spend as much time launching a feature as building it, across disconnected tools. Every tool switch adds hidden costs: reformatting time, mismatched design tokens, lost context, and delayed alignment. Replit Agent 4 generates stakeholder decks, live dashboards, and animations directly from your existing project.

The New Stack

With Claude Managed Agents, Anthropic wants to run your AI agents for you

Anthropic on Wednesday launched the public beta of Claude Managed Agents, a new service that allows businesses to quickly build The post With Claude Managed Agents, Anthropic wants to run your AI agents for you appeared first on The New Stack .

TechCrunch AI

Astropad’s Workbench reimagines remote desktop for AI agents, not IT support

Astropad’s Workbench lets users remotely monitor and control AI agents on Mac Minis from iPhone or iPad, with low-latency streaming and mobile access.

HF Daily Papers

TwinLoop: Simulation-in-the-Loop Digital Twins for Online Multi-Agent Reinforcement Learning

Decentralised online learning enables runtime adaptation in cyber-physical multi-agent systems, but when operating conditions change, learned policies often require substantial trial-and-error interaction before recovering performance. To address this, we propose TwinLoop, a simulation-in-the-loop digital twin framework for online multi-agent reinforcement learning.

Apple ML Research

Governance-Aware Agent Telemetry for Closed-Loop Enforcement in Multi-Agent AI Systems

Enterprise multi-agent AI systems produce thousands of inter-agent interactions per hour, yet existing observability tools capture these dependencies without enforcing anything. OpenTelemetry and Langfuse collect telemetry but treat governance as a downstream analytics concern, not a real-time enforcement target. The result is an “observe-but-do-not-act” gap where policy violations are detected only after damage is done.

Claude Cheat Sheet: A Complete Guide to Anthropic’s AI - eWeek

Claude Cheat Sheet: A Complete Guide to Anthropic’s AI eWeek

07/04/2026

Anthropic's Project Glasswing - restricting Claude Mythos to security researchers - sounds necessary to me

Anthropic didn't release their latest model, Claude Mythos ( system card PDF ), today. They have instead made it available to a very restricted set of preview partners under their newly announced Project Glasswing . The model is a general purpose model, similar to Claude Opus 4.6, but Anthropic claim that its cyber-security research abilities are strong enough that they need to give the software industry as a whole time to prepare.

07/04/2026

Fake Gemini npm Package Steals AI Tool Tokens - gbhackers.com

Fake Gemini npm Package Steals AI Tool Tokens gbhackers.com

07/04/2026

The Best AI Tools in 2026 - BBN Times

The Best AI Tools in 2026 BBN Times

Google AI Edge Gallery

Google AI Edge Gallery Terrible name, really great app: this is Google's official app for running their Gemma 4 models (the E2B and E4B sizes, plus some members of the Gemma 3 family) directly on your iPhone. It works really well. The E2B model is a 2.54GB download and is both fast and genuinely useful. The app also provides "ask questions about images" and audio transcription (up to 30s) with the two small Gemma 4 models, and has an interesting "skills" demo which demonstrates tool calling agai

I tested speculative decoding on my home GPU cluster. Here's why it didn't help.

I spent Saturday night testing n-gram speculative decoding on consumer GPUs. The claim: speculative decoding can speed up LLM inference by 2-3x by predicting future tokens and verifying them in parallel. I wanted to see if that holds up on real hardware running diverse workloads. For the most part, it doesn't. But the journey was worth it, and I caught a benchmarking pitfall that I think a lot of people are falling into. The setup My home lab runs Kubernetes on a machine called Shadowstack. Two

Cleanup Claude Code Paste

Tool: Cleanup Claude Code Paste Super-niche tool this. I sometimes copy prompts out of the Claude Code terminal app and they come out with a bunch of weird additional whitespace. This tool cleans that up. Tags: tools , claude-code

Eight years of wanting, three months of building with AI

Eight years of wanting, three months of building with AI Lalit Maganti provides one of my favorite pieces of long-form writing on agentic engineering I've seen in ages. They spent eight years thinking about and then three months building syntaqlite , which they describe as " high-fidelity devtools that SQLite deserves ". The goal was to provide fast, robust and comprehensive linting and verifying tools for SQLite, suitable for use in language servers and other development tools - a parser, forma

05/04/2026

HN Coding Agents

Tell HN: Anthropic no longer allowing Claude Code subscriptions to use OpenClaw

Received the following email from Anthropic: Hi, Starting April 4 at 12pm PT / 8pm BST, you’ll no longer be able to use your Claude subscription limits for third-party harnesses including OpenClaw. You can still use them with your Claude account, but they will require extra usage, a pay-as-you-go option billed separately from your subscription. Your subscription still covers all Claude products, including Claude Code and Claude Cowork. To keep using third-party harnesses with your Claude login,

Inside The Claude Mythos Leak Why Anthropic S Next Model Scared Its Own Creators

Originally published on CoreProse KB-incidents On March 26–27, 2026, Anthropic — the company known for “constitutional” safety‑first LLMs — confirmed that internal documents about an unreleased system called Claude Mythos had been accidentally exposed online. [2][6] These drafts describe Mythos as Anthropic’s most capable model to date , assigned a risk level the company had never used before and explicitly labeled “too powerful” for broad public release. [2][3][6] That judgment comes from Anthr

5 Prompt Mistakes That Make AI Generate Worse Code (With Fixes)

After hundreds of AI-assisted coding sessions, I've noticed the same five mistakes killing output quality. Each one is easy to fix — once you see it. 1. Dumping the Entire File as Context The mistake: Pasting 500 lines of code and saying "fix the bug." Why it fails: The model spreads attention across irrelevant code. It might "fix" something unrelated or miss the actual issue buried in line 347. The fix: Extract only the relevant function + its dependencies. Add a one-line description of what it

The “Token Bleed”: How to Operate LLMs Without Bankrupting Yourself

Experts across infra, SRE, and product‑engineering circles don’t have one single “rulebook,” but the consensus from real‑world write‑ups and discussions is clear: if you’re building an “AI wrapper” or LLM‑based product, the way you succeed (and avoid backlash) is by focusing on the hard infrastructure and reliability problems , not just the UI or “vibe.” We learned this the hard way. In one project we ran, we watched a single runaway agent hit six figures in tokens before the dashboard even refr

KV Cache Is Why Your Model Fit Until It Did Not

The model loaded. The first prompt worked. Then longer prompts or multiple users showed up, and suddenly the same setup stopped feeling stable. A lot of the time, that is KV cache. What KV cache changes more context means more memory tied up during generation more concurrent requests make the problem worse a setup that fits one short prompt can fail on real workloads people blame the model when the cache is the thing quietly growing The common mistake People test with one short input and assume

HN Coding Agents

A case study in testing with 100+ Claude agents in parallel