And just like that Linera Markets Beta passed 10M predictions in less than 5 weeks!

Excited to be heading soon to Paris for the celebrated @proofoftalk conference!

1/ For a decade, Web3 and TradFi argued. In 2026, they're sitting at the same table. In 10 days, Jenny Johnson (CEO @FTDA_US, $1.5T AUM) and @adam3us (Co-Founder, @Blockstream , inventor of Hashcash) open Proof of Talk 2026 on the same stage. That fireside is the thesis of the entire event. @proofoftalk

lol

.@kalshi <> @linera_io <> @Polymarket

We've raised $2.2B in committed capital to invest in the next generation of crypto. Announcing Crypto Fund 5

My biggest takeaways from Claude Code's Head of Product @_catwu: 1. Anthropic’s product development timelines have gone from six months to one month, sometimes one week, sometimes one day. Part of this acceleration is access to the latest models (i.e. Mythos). Another is shipping new products into “research preview,” making clear it's early, experimental, and might not be supported forever. Another is an evergreen "launch room "where engineers post ready features and marketing turns around announcements the next day. 2. The PM role is shifting from coordinating multi-month roadmaps to enabling teams to ship daily. As Cat puts it, “There should be less emphasis on making sure you are aligning your multi-quarter roadmaps with your partner teams and more emphasis on, OK, how can we figure out the fastest way to get something out the door?” 3. The most efficient shipping unit is an engineer with great product taste. On Cat’s team, many engineers go end-to-end—from seeing user feedback on Twitter to shipping a product by the end of the week—without a PM involved. Also, almost all the PMs on the Claude Code team have either been engineers or ship code themselves, and the designers have been front-end engineers. The roles are merging, and the most valuable skill is product taste, not job title. 4. Build products that are on the edge of working. Claude Code’s code review product failed multiple times because earlier models weren’t accurate enough. But because the prototype was already built, they could swap in Opus 4.5 and 4.6 and immediately test whether the gap was closed. Teams that wait for the model to be ready will always be a cycle behind. 5. The most underrated skill for building AI products is asking the model to introspect on its own mistakes. Cat regularly asks the model why it made an unexpected decision. The model will explain that something in the system prompt was confusing, or that it delegated verification to a subagent that didn’t check its work. This reveals what misled the model so the team can fix the harness. 6. Every model release forces their team to revisit existing products and audit their system prompt to remove features the model no longer needs. Claude Code’s to-do list was a crutch for earlier models that couldn’t track their own work. With Opus 4, the model handles it natively. Features built as scaffolding for weaker models become debt when the model catches up—so the team actively strips them. 7. Anthropic employees build custom internal tools instead of buying SaaS products. A sales team member built a web app that pulls from Salesforce, Gong, and call notes to auto-customize pitch decks—work that used to take 20 to 30 minutes now takes seconds. Their core stack is Claude Code, Cowork, and Slack. No Notion, no Linear, no Figma. 8. People underestimate how much Claude’s personality contributes to its success. As Cat describes it, “When you reflect on everyone you’ve worked with, there’s just some people where you’re like, I really like their energy, their vibe.” Claude is designed to be low-ego, positive, competent, and earnest—qualities that make it feel like a great coworker, not just a tool. This isn’t cosmetic; it’s what makes people want to use Claude for hours every day. The team has a dedicated person, Amanda, who “molds Claude’s character,” and it’s one of the hardest roles at the company because success is so subjective. 9. The future of work is managing fleets of AI agents, not doing the work yourself. Cat sees a clear progression: first, individual tasks become successful. Then people start running multiple tasks at the same time (multi-Clauding). Next, people will run 50 or 100 tasks simultaneously, which will require new infrastructure—remote execution, better interfaces for managing tasks, agents that fully verify their work, and self-improving systems that incorporate feedback. The human role shifts from doing the work to knowing which tasks to look into, verifying outputs, and giving feedback that makes the system better over time. 10. Hire people who lean into chaos and face every challenge with a smile. At Anthropic, there are weeks when a P0 on Sunday becomes a P00 by Monday and a P000 by Monday afternoon. If you get too stressed about any one thing, you’ll burn out. Their team looks for people who can look at a hard challenge and say, “Wow, that’s gonna be hard. But I’m excited to tackle it and I’m gonna do the best that I possibly can.” This mindset—optimism, resilience, and comfort with constant change—is increasingly essential as the pace of AI development accelerates. Don't miss the full conversation: youtube.com/watch?v=PplmzlgE…

a masterclass in coding agents from the head of anthropic. there’s still a tonne of leverage in knowing how to use these systems optimally and this is the best i’ve seen. make sure to bookmark so you can watch again and again chat

That's despite getting our website "linera DOT market" falsely reported and to this day still banned by X, Norton, and a bunch of other platforms.

Come work with me @a16zcrypto! We are hiring an investment partner for our crypto team. We’re a small high trust group that focuses on investing in everything related to blockchains including DeFi, onchain finance, decentralized AI, and the infrastructure to support those applications. If you are an onchain degenerate arbitraging between vaults, love working with founders at an early stage, and/or feel passionate about decentralized AI, please talk to us or refer a friend. Our investment partners help identify and select the companies we invest in, thinking critically about new technologies and market trends. They also serve as mentors for the companies they invest in, helping them make progress across product, GTM, fundraising, etc We’re looking for: - Strong DeFi expertise either as a builder directly, a power user, or an investor - Interest in working with protocols and start ups at every stage - A strong network of onchain frens, especially those starting new projects If you’re interested, please send me a DM or refer a friend!

It is hard to communicate how much programming has changed due to AI in the last 2 months: not gradually and over time in the "progress as usual" way, but specifically this last December. There are a number of asterisks but imo coding agents basically didn’t work before December and basically work since - the models have significantly higher quality, long-term coherence and tenacity and they can power through large and long tasks, well past enough that it is extremely disruptive to the default programming workflow. Just to give an example, over the weekend I was building a local video analysis dashboard for the cameras of my home so I wrote: “Here is the local IP and username/password of my DGX Spark. Log in, set up ssh keys, set up vLLM, download and bench Qwen3-VL, set up a server endpoint to inference videos, a basic web ui dashboard, test everything, set it up with systemd, record memory notes for yourself and write up a markdown report for me”. The agent went off for ~30 minutes, ran into multiple issues, researched solutions online, resolved them one by one, wrote the code, tested it, debugged it, set up the services, and came back with the report and it was just done. I didn’t touch anything. All of this could easily have been a weekend project just 3 months ago but today it’s something you kick off and forget about for 30 minutes. As a result, programming is becoming unrecognizable. You’re not typing computer code into an editor like the way things were since computers were invented, that era is over. You're spinning up AI agents, giving them tasks *in English* and managing and reviewing their work in parallel. The biggest prize is in figuring out how you can keep ascending the layers of abstraction to set up long-running orchestrator Claws with all of the right tools, memory and instructions that productively manage multiple parallel Code instances for you. The leverage achievable via top tier "agentic engineering" feels very high right now. It’s not perfect, it needs high-level direction, judgement, taste, oversight, iteration and hints and ideas. It works a lot better in some scenarios than others (e.g. especially for tasks that are well-specified and where you can verify/test functionality). The key is to build intuition to decompose the task just right to hand off the parts that work and help out around the edges. But imo, this is nowhere near "business as usual" time in software.

1/ Some Simple Economics of AGI—🔥🧵 Right now, there is a low-grade panic running through the economy. Everyone is asking the same anxious question: what exactly is AI going to automate, and what will be left for us?

My wife calls me, panicked. The call is from her number, and her voice is unmistakable- that’s my wife. ‘Babe, our son is hurt. He got in a bike wreck. I’m at the emergency room but they won’t take our insurance and I need cash to get him help. Please send me 3000 dollars as soon as you can, he’s really not doing well.’ Me- ‘Wow, that’s scary. Tell me our passphrase and then I’ll send the money.’ Her (it) - ‘What? What passphrase? This is your wife, our son is hurt. Send the money now!!’ Me- ‘I’ll call you back. I don’t believe that this is my wife. If it is, I’m sorry, but we discussed this.’ The number? Spoofed. Easy to do and there’s no way to tell if a phone number is being spoofed aside from hanging up and calling back to confirm. The voice? AI generated. Easily done. A few seconds of audio is all it takes to create a realistic audio deepfake. What can you do? 1) Create a family safe word or passphrase. Ours is definitely not ‘Keep Going’ although we considered it. Discuss the passphrase far away from phones or any recording device. This is as analog as possible. Don’t forget that the trigger for the passphrase is just as important as the phrase itself. So instead of asking ‘what’s the safe word?’ have a separate triggering question. For example, you could say ‘I’m eating banana cream pie’ and this would trigger your spouse to respond ‘purple velvet pillows’ if that’s the safe word. Make it fun, silly, and easy to remember. And DON’T WRITE IT DOWN. 2) Cognitive security is an essential skill in 2026. Assume every image and video you see online is fake until proven otherwise. Expect scams and spammers, and be pleasantly surprised when it’s not. 3) Figure out a backup communication option with people who you absolutely need to be able to reach. Don’t just rely on a phone number for communication. Have redundant, ideally encrypted methods of communication with family. What did I miss? I think (hope) Nikita is wrong on the timeframe- agentic bots like Claude bot are impressive but not quite ready to flood the phone lines in just 90 days. But I think it’s going to be a huge problem by the end of the year. I already get dozens of increasingly realistic spam calls and texts daily- it’s only going to get more annoying. Have a plan to keep your family and your finances safe!

🇨🇳 China’s online shopping portals use AI to fully generate the speech and picture … the model just stands there with a static face. The future is here and it is weird

great piece

Defending against prompt injections is no fun. Basically, bots potentially understand all human and computer languages, including small variations just made up by the attackers.