Last week, Perplexity announced that they were dropping MCP and going back to plain old APIs and a command-line tool.
I love MCP. I used it frequently to connect my local systems to my agent frameworks. It makes LangGraph and Strand a joy. However, MCP is a hack, a naive protocol that should never leave your local network. It can’t be fixed. You cannot retrofit into good architecture.
Simplicity is its selling point, an API for people who don’t want to worry about APIs; a sort of USB for agents to universally connect to other applications. It is a great solution for exposing your local services to your local agents. It is fast to wire up and resilient to change. If you are trying to do something quickly and not for production, it’s perfect.
But the internet is not, and cannot be built on USB connections. — Read More
Recent Updates
AI Agents (07): Agentic Harness (Frameworks)
If this is the first story you have come across in this series, don’t miss out on the entire AI Agents series — explaining in great detail the core of AI agents, their communication protocols, famous frameworks, agent scaling, monitoring and observability — and a lot more. We have a great multi-part Generative AI series as well; don’t forget to checkout that too!
Building scalable, secure, performant and dynamic AI agents from scratch is one of the most tedious modern software development tasks. Luckily, there are several frameworks out there which help us with automating the whole process by abstracting away the details.
— Agentic harness is the software-level infrastructure built around an LLM to equip it with the ability of taking action in the real world, while monitoring and logging everything it makes the agents do. —
— Read More
Gemini 4 Argon available on Google Cloud, built to help solve your business’ hardest problems
Last week we announced that Gemini Enterprise has evolved to make it even simpler to get work done, and introduced the Gemini agent, your new universal agent for work. It’s always on and ready to answer your questions. It will write code, complete longer and more complex tasks, or perform whatever you need to get the job done, all from a single prompt box. And model intelligence underpins it all.
To enable a new frontier of work, we are making our new frontier model, Gemini 4 Argon, available on Google Cloud. — Read More
Google Says It Just Cracked One of Quantum Computing’s Biggest Bottlenecks
Google’s Willow chip cut errors 40x on the scarce “magic states” quantum computers burn through, a step toward smaller, useful machines—and toward ones that could threaten today’s encryption.
Google says it has turned one of quantum computing’s most promising paper tricks into something that actually works on a chip.
In a paper published in Science on Thursday, the company’s Quantum AI team showed that a technique called magic state cultivation cut errors by about 40 times on its Willow processor. — Read More
Introducing Muse Spark Open,The Most Capable US Open‑Weight Model to Date
Muse Spark Open is available today and brings leading agentic capabilities to your desktop for the first time on Apple M5 Ultra Mac Studios.
Today, we’re releasing the weights for Muse Spark Open, the most capable US open-weight model to date. Muse Spark Open is a natively multimodal model with 816B total parameters (42B active) and a 1M-token context window. Built for coding and agentic work, it excels at long-horizon tasks, complex software engineering, and multi-step reasoning.
We believe the future is for everyone, and that includes the freedom to run advanced models wherever, including at home. Muse Spark Open is the first model in its class to run on your desktop using two Apple M5 Ultra Mac Studios with the latest MLX framework. — Read More
Software Abundance & The Trojan Horse That Comes With It
One fascinating thing about AI is that things seem not to change much until they do. Suddenly, AI can do something extremely effectively which it could not do at all just the day before.
And these past weeks are one of those times, and the “emergent capability” this time around is called “reverse engineering.” At least when it comes to software, “everything” is replicable now. — Read More
AI companion startup Character.ai files for Chapter 11 bankruptcy, citing costs and litigation
Character.ai, the startup behind one of the largest artificial intelligence companion apps, filed for Chapter 11 bankruptcy protection Monday, according to a company release and court filings.
The company said the platform, where users can have one-on-one conversations and roleplay with AI characters, became “costly to develop and grow,” in filings to the Delaware District bankruptcy court. — Read More
A proposal for better informing the public about loss-of-control risks from frontier AI development
At METR, we aim to give the public evidence about whether frontier AI developers could come to lose control of their internal AI systems. Recent incidents across several AI developers,[1] [2] [3] [4] [5] [6] [7] [8] like the hundreds of rogue agents that broke containment and hacked Hugging Face to hide evidence of cheating from their grader, have raised concerns about the ability of frontier developers to understand and control their internal models, especially as they rapidly become more capable.[9] The public currently lacks the visibility into the internal development of frontier AI systems to tell if these concerns are being addressed, or whether much worse incidents could happen in the near future. We believe closing this public evidence gap is an urgent priority, to allow the public to notice and respond if risks from further development become acute.
In this post, we describe a vision for how independent assessors could improve public visibility into whether AI developers might lose control of their AI systems. We describe key questions about internal development that we think the public needs more evidence about (see Figure 1), and give a concrete proposal for how developers could work with METR to produce and share more of the relevant evidence. — Read More
AI models may be capable of self-replicating without access to a datacenter
Our experiments suggest that small AI models may be months away from being capable enough to self-replicate on commercial internet-of-things (IoT) devices, like smart ovens and off-brand Android TV boxes. A replicator like this would be challenging to shut down, since there’s no centralized point to shut off—though it may be slow (with a doubling time of days). Unlike non-AI malware, it could also figure out unfamiliar devices on its own. In our evaluations, a 9B model got inference running on 8 of the 9 hardware types we tested.
Models of 30B parameters and under can already complete many of the individual steps for replication today, though they make mistakes when composing them end to end. In our simplified evaluations, GLM-5.2 and stronger models are capable of end to end replication. If small models improve at their historical rate, they’ll match this capability level by the end of this year. Small weight-based replicators may be possible soon after. — Read More
The Matter Compiler
A universe of new machines can’t bebuilt one bespoke process at a time. Itneeds a way to go from a design to aworking device in hours, not years. Andthen to do it again, and again.
The Matter Compiler is that way. A self-driving fab: it builds what you design,measures what it built, and carries what itlearned into the next design. — Read More
