Filippo Facioni

Cloud agents are powerful: task parallelisation, self-verification, long-running. But they're missing a UX that feels local. So we built the @twill_ai CLI The Twill CLI lets you create and manage remote coding agent sessions from your terminal while @AnthropicAI Claude Code, @opencode , or @OpenAI Codex runs the work inside persistent cloud sandboxes. - Create and manage tasks from the CLI - Switch between agent modes like plan, code, and ask - Keep the familiar terminal workflow while the runtime lives in the cloud - Run longer-lived and parallel work without tying it to your laptop Try it at twill.ai

@bencera @polsia I literally spent 5 min looking for the chip tune version of the song. mind sharing it? haha

@tryhardfifi @polsia i love that you love it. spent way too much time on the about page haha

in a world full of stuff without taste, be @bencera building the about page of @polsia

We just raised $6.3M seed to help dev teams build and deploy MCP Servers for AI Agents and MCP Apps for ChatGPT / Claude Our open source SDKs crossed 4M downloads when we recorded this video (now already 6M+). Our mission is to build tools that enable humans to build software for agents, as agents will be the primary consumers and manipulators of digital information. We make building with MCP effortless. Create MCP servers or MCP Apps with mcp-use SDK. Preview and iterate with mcp-use Inspector. Connect your GitHub repo to our Manufact Cloud. Instantly make your MCP Server available to billions of AI agents and 800M+ users of ChatGPT / Claude.

@manufact lets go booiiiiiiis

Recently, Anthropic anthropic.com/engineering/co… and Cloudflare blog.cloudflare.com/code-mode/ released two blog posts that discuss a more efficient way for agents to interact with MCP servers, called Code Mode. There are three key issues when agents interact with MCP servers traditionally: - Context flooding - All tool definitions are loaded upfront, including ones that might not be necessary for a certain task. - Sequential execution overhead - Some operations require multiple tool calls in a chain. Normally, the agent must execute them sequentially and load intermediate return values into the context, wasting time and tokens (costing both time and money). - Code vs. tool calling - Models are better at writing code than calling tools directly. To solve these issues, they proposed a new method: instead of letting models perform direct tool calls to the MCP server, the client should allow the model to write code that calls the tools. This way, the model can write for loops and sequential operations using the tools, allowing for more efficient and faster execution. For example, if you ask an agent to rename all files in a folder to match a certain pattern, the traditional approach would require one tool call per file, wasting time and tokens. With Code Mode, the agent can write a simple for loop that calls the move_file tool from the filesystem MCP server, completing the entire task in one execution instead of dozens of sequential tool calls. We implemented Code Mode in mcp-use's (repo github.com/mcp-use/mcp-use ) MCPClient . All you need to do is define which servers you want your agent to use, enable code mode, and you're done! The client will expose two tools: - One that allows the agent to progressively discover which servers and tools are available - One that allows the agent to execute code in an environment where the MCP servers are available as Python modules (SDKs) Is this going against MCP? Not at all. MCP is the enabler of this approach. Code Mode can now be done over the network, with authentication, and with proper SDK documentation, all made possible by Model Context Protocol (MCP)'s standardized protocol. This approach can make your agent tens of times faster and more efficient. Hope you like it and have some improvements to propose :)

I’m in a wedding in mexico. No one knows their jobs are replaced by AI

@DhruvRoongta Why not the other way around? Sheer size?

Counter-intuitive, but its likely that ChatGPT will become like Google (heavy consumer-facing) and Anthropic like Microsoft (heavy business-facing)

@joshm Finally. Now I can try going back :)

iCloud-based Passkeys are now supported in Dia & Arc!

@Kimi_Moonshot Fucking unbelievable, gg kimi team

🚀 Hello, Kimi K2 Thinking! The Open-Source Thinking Agent Model is here. 🔹 SOTA on HLE (44.9%) and BrowseComp (60.2%) 🔹 Executes up to 200 – 300 sequential tool calls without human interference 🔹 Excels in reasoning, agentic search, and coding 🔹 256K context window Built as a thinking agent, K2 Thinking marks our latest efforts in test-time scaling — scaling both thinking tokens and tool-calling turns. K2 Thinking is now live on kimi.com in chat mode, with full agentic mode coming soon. It is also accessible via API. 🔌 API is live: platform.moonshot.ai 🔗 Tech blog: moonshotai.github.io/Kimi-K2/thinki… 🔗 Weights & code: huggingface.co/moonshotai

@livinoffwater Were you at the agentmail hackathon?

Big day today. Wish me luck 🙏 Doing IRL demos all day.

@nntaleb Why not use a kindle? Never went back to analog books

Never underestimate the multiplicative property of books; expandable bag is now >21kg PS: Please don't tell the airline.

@RolandForTexas @elonmusk Either this is rage bait or you still don’t understand that wealth is not a zero sum game. We should have more trillionaires, not less.

@elonmusk Trillionaires shouldn’t exist. Especially while people are starving.

Elon Musk wants to become a trillionaire off the backs of American taxpayers while we are struggling just to pay for groceries.

Today, BillionToOne went public, becoming YC's 4th biotech IPO. As a company that quietly provides societally important infrastructure, BillionToOne is less well known than they deserve to be. Here is their story, from grad students to IPO. BillionToOne is the rare company that is both incredibly good for the world and also an extremely good business. 1 in 11 pregnancies in the US take the BillionToOne genetic test, and through this they prevent an immense amount of human suffering. But BillionToOne is also the rare biotech that has generated revenue from the early days. It has always had excellent margins, rapid growth, and capital efficiency. Today, it is going public not just as a scientific success, but a commercial one, with over $265M in ARR and 65% gross margins. We met the BillionToOne founders, Oguzhan and David, when they were still finishing up their PhDs. At the time, BillionToOne was only an idea, but it was a thought provoking one. When a woman is pregnant, fragments of the fetus' DNA circulate in her blood. What if you could design a genetic test for the fetus using this free circulating DNA? That would allow you to do pre-natal genetic testing with a simple blood draw. The problem is that this DNA is a mess - it's tiny snippets mixed in with a much larger amount of unrelated fragments. To extract the signal from the noise requires both advanced wet lab sequencing techniques and also advanced machine learning algorithms. Oguzhan and David were the exact right people to crack this because they had both biology and CS backgrounds. The BillionToOne founders have always moved fast. In just 6 months from YC funding them, while still finishing their PhDs, they went from an idea to a proof of concept. From there, it was just two years to regulatory approval and a commercially available test. They haven't slowed down. While the pre-natal genetic testing market they started in is over a $2B market and still mostly untapped, they've already expanded to a second market in oncology. It turns out that their same technology that makes sense of free floating DNA can also be used to detect cancer from a blood test. The potential market size for that is enormous - perhaps $100B. That is where BillionToOne is going, and it's quite possible that if you're reading this, you will someday regularly take their blood test to stay cancer free. To all the people who say that Silicon Valley just funds GPT wrappers and B2B SaaS, BillionToOne should be a star example of how the SV ecosystem can solve societally important problems. It is a classic story of how a highly technical team, an ambitious idea, and a small amount of funding can catalyze the creation of enormous value.

I can’t believe this worked. Let’s breakdown the cost: - sharpie $2.99 - donuts $17.99 - cardboard free (thx wholefoods) conversion rate of people that mattered talking to me: ~0.5%

@computeless Fua super hard decision. I think the right one is better

what to use for the new dmg background image... hmm...

Tornyol (@tornyolsystems) is building micro-drones that kill mosquitoes. They use smartphone microphones, car park assist sensors, and some clever DSP and control to transform 40-gram toy drones into mosquito killers.