Vinoo Ganesh

@thinkymachines (8/8) Infrastructure was the blocker. @thinkymachines removed it. Six months became six iterations. You changed what's possible with personalized AI. Thanks, Tinker team.

(1/8) @thinkymachines just gave the Keru.ai team early access to Tinker and it changes everything around how we personalize AI agents. Here's how.

@thinkymachines (7/8) Now when our customers use our platform, their votes actually matter. The model learns from them. Their model gets better every time they use it. That's defensible. That's why they don't leave.

(6/8) Then we got early access to Tinker. Instead of building distributed training systems, you literally just write: await training_client.forward_backward_async(user_preference_data) await training_client.optim_step_async(learning_rate=5e-5) Tinker handles logprobs, versioning, real-time orchestration, reliability. You define what to optimize for. It works. It took me 6 runs to get what I wanted, and it's simple.

@thinkymachines (5/8) Build it yourself? 6 months. $500K. 2-3 engineers. OpenAI's API doesn't support it. AWS SageMaker? 3+ months of setup for the same problems. We were looking at half a year delay or accepting a frozen model.

(4/8) The problem is the infrastructure: You need logprobs access (most APIs don't expose them). Custom loss functions (generic APIs only have cross-entropy). Real-time training pipelines (can't batch). Per-analyst model management (versioning nightmare). Fault tolerance at scale (one failure cascades everywhere).

(3/8) Real learning requires reinforcement learning. You capture what users prefer in real-time, update the model per-user, keep improving it. Analyst A wants concise? Their model learns that. Analyst B wants deep analysis? Their model learns that. They can't switch platforms, they'd lose all that personalization.

(2/8) First, some context. We were on a call with the head of data analytics at a large financial institution. He said: "The funny thing about every single product demo I've seen is that the AI never actually learns." He was right. Most companies do fine-tuning. Train once on labeled examples, freeze it, same output for everyone forever.