alex

What’s the value capture play if that is the case? 🏇📉

Btw, if Anthropic had any way to ship this, they would. Trained AI models are the fastest depreciating asset in history. GPT-4 cost $100M to train 2 years ago and now it's worth less than Qwen3.5-27B ($1M). Sending the FOMO back, clock is ticking boys. @DarioAmodei @bcherny

Activity ≠ Productivity

@MeekMill Make sure to put your token in a .env

I need a GitHub too! Is it like that or nah?

We should make EnterpriseClaw just for the lolz. Java 21, Spring Boot, 14 abstract factory beans, 2GB Docker image, takes 45 seconds to start, AbstractSingletonProxyFactoryAgentClawResponseHandlerBeanDefinitionRegistryPostProcessorImpl .java

@arlanr U do chunk citation?

try it yourself (1000 free credits for feedback): trynia.ai blog: trynia.ai/blog/tree-reas…

we built a new way to index and chat with any PDF. introducing tree-guided hybrid agentic search. @nozomioai indexes PDFs into document trees, reasons over structure, and uses agents to retrieve precise answers in minutes. we’re seeing strong performance on financebench and other benchmarks. curious what you think!

@ericzakariasson Is there a hook after a lan is generate??

what are agent hooks, and why should i use them? when you understand how they work they can actually do a lot of heavy lifting for you. here are 5 use cases of hooks ↓

@mntruell can we add a pre and post hook for plan mode? Pretty please

@gokulr @grok tldr

VERTICAL AI CHALLENGE Vertical AI Founders: You've spent 2+ years building your agents, training your model on your customers' data, embedding into workflows, creating a powerful GTM motion, all the best practices. You've beaten back challengers and are the #1 or #2 player in your vertical. I'm sorry, you cannot relax. In fact, you need to massively up your game. Turns out you are facing an existential challenge: long-horizon agents (eg: Claude Code). Agents that are not trained on a specific domain, but can reliably work for hours or days on end in pursuit of a goal, self-correct, and actually do stuff. I'm sure many Vertical AI founders will say: "Oh, we are not worried. We are the system of record for decision traces. We train on enterprise-specific context. That's why these horizontal agents can never catch up with this." You might well be right. But, but, but ... you cannot afford to bury your head in the sand. These long-horizon agents will get better very, very quickly. You need to understand precisely how good they are at the exact jobs you've built your agents on. You cannot wait for someone else to do this. For example, if you're a legal AI company with an agent that automates contract review, you must compare how good your specialized agent is versus a general-purpose long-horizon agent that's simply given the contract and asked to perform the same review. My challenge to you: Assign a strong engineer on your team to focus 100% on using long-horizon agents (with minimal context, other than just the contract in the example above) to compete with your custom-trained agents. Benchmark how the long-horizon agents perform vs your agent. Rinse and repeat it every few months. Like with most other things worth measuring, what matters is the rate of improvement (the "slope" vs the Y-intercept). If the long-horizon agent is 30% as good as your vertical agent on Day 1, but 50% as good on Day 60, and 70% as good on Day 120, you need to reassess your product strategy. AGI is coming for everyone. Long-horizon agents are the closest we have to AGI, and as a Vertical AI company, you need to figure out how you compete and survive. Game on.

Can you pls make the “Set due date” button as a default option when creating a ticket @linear While we’re at it, can you make today a default in the due date options 🙏

@AndyT9000 @thdxr @rauchg It’s the only angle

@thdxr @rauchg Test driven development might be the most powerful angle right now.

Narrative violation: AI coding will result in the most rigorously tested, type-checked, and provably correct code in the world. First, the testing and verification feedback loop helps agents and prevents slop. Second, tests have always been a chore, but who doesn't love ✅ a high test count and checkmarks? Agents are glad to write as many tests as we want.

what's a DSPY for agents?

*so exciteeeeed*

Hackathon day at @ClarityCareAI HQ 🚀🚀

🐻

@wivincent *Perspective clients

its all about perspective c u tmr ~

@ClarityCareAI processed requests 🚀

⁦@wivincent⁩ enjoying the view

@wivincent Who’s care is this??

focus c u tmr ~