~/context-window $ _

The Context Window

50 engineers. Production AI. India. Invite-only.

Not a newsletter. Not a Discord for sharing papers nobody reads.

Every Monday morning someone posts what broke in their production system last week. Not a write-up. Not lessons learned. The actual failure, the debugging session, the thing no documentation prepared them for.

We are building the founding cohort now. Ten people who set the culture for everyone who comes after.

// admission

01SDE3 or above in an AI or ML role
02has shipped AI to production — real users, not a demo
03referred by an existing member or invited directly
apply →

// what actually happens here

#what-broke — every monday

One post per week. What broke, how you found it, what it took to fix it. This is where the knowledge that never makes it into blog posts actually lives.

#deep-dive — monthly

One member walks through a hard problem they are working on. 20 minutes, then the room picks it apart. No slides required. Closest thing to free consulting with people who have done the same work.

#compensation — anonymous

We are building the most honest AI engineer compensation database in India. Member-only, anonymous, aggregated. The number that does not exist anywhere publicly.

// the kinds of conversations

1.

Our retrieval precision was 0.71 in staging and 0.43 in prod. Three hours of debugging. Here is why.

2.

Replaced LangChain with direct API calls. 40% less code. Was it worth the migration pain?

3.

What does ML model monitoring actually look like at a company that is not Big Tech?

4.

We re-finetuned the same base model four times in six weeks. Here is what we got wrong each time.

5.

Staff AI engineer at a Series B in Bangalore. Total comp breakdown, honest.

// tools we actually talk about

LangGraphLangSmithLangFuseDSPyInstructorPydantic AILlamaIndexRagasBraintrustWeights & BiasesvLLMSGLangTGIOllamaRay ServeAxolotlUnslothTRLQdrantpgvectorAnthropic SDKOpenAI SDKMLflow

// founding cohort

First 10 members set the culture. Applications are reviewed manually.

Not everyone gets in.

apply →