The Viral Github Repo To Reverse Engineer Anything

Reverse Engineer Anything" (REA) is a Model Context Protocol (MCP) that enables AI agents to virtually rebuild software by deactivating source apps and leveraging established tools like Ghidra, Hopper, and IDA. While it has sparked a massive online craze and numerous demos on social media, the underlying technology relies on veteran decompilation tools rather than entirely new breakthroughs. The speaker views the trend less as an unprecedented technological revolution and more as a cultural mimetic moment driven by high engagement on platforms like X.

Meta releases its first frontier model, Muse Wonder

Meta’s Superintelligence Lab released a pair of new state-of-the-art models on Monday, Muse Wonder and Muse Wonder Full, representing the company’s first attempt at a frontier model.

Where Muse Spark aimed to be a lightweight model that could perform at low latency, the Wonder models are larger and said to be more capable, with higher benchmark scores and better results on complex tasks like coding.

Insane Jev Use Cases You Need To Use Right Now

AI LABS explores integrating the decision-making model Jev with coding agents to optimize workflows. This guide details seven practical use cases for implementing Jev to enhance speed and reduce costs when managing tasks like compaction, skill selection, file searching, code reviews, browser testing, and enforcing project rules.

Anthropic launches Claude Haiku 5.5

Anthropic today released Claude Haiku 5.5, cutting token prices by 90% for requests below 100,000 tokens and targeting the repetitive work that can make enterprise AI expensive at scale: summarizing documents, classifying information, querying databases and handling smaller assignments for more capable agents.

The model starts at $0.10 per million input tokens and $0.50 per million output tokens, matching OpenAI’s GPT-6 Luna. Anthropic estimates that workloads cost approximately 75% less to run than on Haiku 4.5, once request sizes and changes in token consumption are considered.

Mistral’s new 1T model aims to leapfrog closed and open rivals

Amid a growing divide between closed models that can be unplugged and open models that are often made in China, Mistral is positioning ML4 as an alternative to both. Nicknamed Le Chonk in reference to its 1 trillion parameters, ML4 is definitely not small; but is not an open-weight model yet. For the time being, it can only be accessed via a public guardrail endpoint, but Mistral plans to make its weights available in just three weeks, after safety testing is complete.

Reflection AI debuts Beam

Reflection AI is officially unveiling Beam, its first frontier, open-weight AI model. The two-year-old, Brooklyn-based startup claims Beam matches the performance of leading Chinese open models on advanced reasoning benchmarks at dramatically lower costs, a claim that could heat up the race to build a Western answer to DeepSeek, Qwen, and Z.ai.

New Skills! v1.3 brings /pr, /implement-spec, and /retro

Matt Pocock introduces version 1.3 of their skills repository, focusing on workflows for large-scale coding projects and automated retrospective analysis. Learn how these new tools help manage sub-agents, improve pull request documentation, and refine repository health to increase overall development efficiency.