Ryan Levy

Top 10 agent learnings from this week 1. Never tell an agent to "queue" work. Replit told us our approved fixes were "queued." It stopped overnight and built none of them. Now every prompt says "do it now." 2. We moved to cheaper models, and AI spend still went from ~$35 a day to $84. Call volume went up faster than the price per call came down. 3. I told the agent the old model HAD to go. A day later it was still running. I don't count it done until the call log shows zero calls in the last 24 hours. 4. Our AI fact-checker flagged 18 of 30 accurate notes because it treated opinions as factual claims. Every flag triggered a rewrite, so we paid twice for worse copy. 5. Each of the agent's daily reports said "no schema changes." The build broke anyway, on a table left over from an older change. The reports only checked each day's own work. 6. When an agent says data is missing, check the database first. This week the data was there all along, and the step that displays it was what was broken. Check the record before you pay to re-enrich anything. 7. Scout, our email agent, now acts on plain-English replies like "intro me" or "pause." It runs the same code as the buttons in the app, not a separate version. 8. One of our agents was answering emails sent to another agent's inbox for days before we caught it. 9. We canceled Canva after 12 years. We'd stopped using it a year earlier but kept paying, and two of us had separate team plans without knowing. We were paying about 4x for nothing. 10. 10K started as our AI VP Marketing. This week it pulled together SaaStr's financials, and it already runs invoicing, collections and commissions. We never planned any of that.

People should always be in control of the future. OpenAI is against the notion that it would be acceptable for people to be relegated to a role of doing things that “AI systems think makes sense to reward in humans”. People should not cede control of the future to superintelligence, and that requirement should ultimately constrain AI progress.

New Anthropic research: progress on provable inference for large language models. Here, we introduce FlashProve. Building on established cryptographic techniques, FlashProve allows developers to verify whether a given response came from a given AI model. It’s substantially more efficient than prior open-source methods for doing so. These proofs have a range of applications. They can, for instance, make it harder for attackers to hijack a chat, or enable safety evaluators to verify that the model they’re testing is the same one being served to customers. For our full write-up, see here: anthropic.com/research/prova… Further progress in provable inference will likely be an important part of improving trust in AI deployment. To that end, we're open-sourcing FlashProve’s code, here: github.com/anthropics/FlashP… We’re grateful to our collaborators: Alessandro Chiesa, @GiacomoFenzi, @gwberg95, @miranda__christ, Rahul Dalal (rdalal.github.io) and Ben Mayer (@transmissions11).