Avyay Varadarajan @avyvar

co-founder @daridotdev // prev @ycombinator, @caltech, basketball data @lakers, software @uber, startups dari.dev Fremont, CA Joined April 2018

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Avyay Varadarajan @avyvar

2 days ago

(player's association nightmare, but interesting to think about)

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another reason to feel like NBA teams / sports orgs are undervalued: teams capture every player's 3d skeletal data (hawkeye data) at ~25hz in games / practice. if players wear eye-tracking goggles (there are real coaching incentives to this) you get real-world egocentric data

Reka @RekaAILabs

3 days ago

Training world models needs egocentric video and dense action signals, synchronized. That data is genuinely hard to find. We built it from Counter-Strike 2 demos. CS2-10k: 600K+ player-round videos, 10K+ hours, per-frame annotations — keyboard state, mouse delta, 3D position,

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Avyay Varadarajan @avyvar

2 weeks ago

@Walter64456133 If you use the SLM option, you do have the added overhead. However, if you use the heuristic router (a combination of input token + eval scores), there’s negligible latency.

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Avyay Varadarajan @avyvar

2 weeks ago

Token-maxxing is getting out of hand. Most AI apps send every request to the biggest model, even when a smaller model would work. We built Dari Router to fix that.

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Avyay Varadarajan @avyvar

2 weeks ago

@halfdebugged this depends on your router of choice If you use the SLM option, you do have the added overhead. However, if you use the heuristic router (a combination of input token + eval scores), there’s negligible latency. rn, everything is from the global endpoints

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Avyay Varadarajan @avyvar

2 weeks ago

If you're a company struggling with token costs and want us to take a look, book a call. calendly.com/avyay-dari/30m…

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Avyay Varadarajan @avyvar

2 weeks ago

@daridotdev gives you full control over your routing workflows. Use your own traces and evals to build custom routers for each workflow: fine-tuned small-model routers when you need learned behavior, or deterministic policies when you want full control.

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Avyay Varadarajan @avyvar

3 weeks ago

tons of interesting benchmarks you could do within a front office since you have counterfactuals presti-bench: given all the internal data we have on players / how we value draft picks, would you accept this trade proposal?

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Avyay Varadarajan @avyvar

3 weeks ago

there are several interesting basketball benchmarks one could do but would be cool to see how well LLMs perform at predicting rotation charts imperfect information, but given recent stats, lineup data, etc, how close can an LLM emulate coach game-planning

Theo - t3.gg @theo

3 weeks ago

We need more niche benches. We need ios-bench. We need ts-bench. We need baseball-bench. We need yt-thumbnail-bench. We need way more creativity in how we measure what models can do.

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Avyay Varadarajan @avyvar

4 weeks ago

Everyone seems to only focus on sandboxes for agent runtimes but decoupling your agent loop from the sandbox / persisting state via durable execution is tablestakes for long-running agents, as seen w/ Cursor if you want to scale long-running agents try us out @daridotdev!

Cursor @cursor_ai

4 weeks ago

A great cloud agent experience involves a lot more than moving a local agent to a server. We've learned that it requires a durable execution platform, a powerful harness, and the tools and infra to give agents realistic development environments. cursor.com/blog/cloud-age…

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Avyay Varadarajan @avyvar

4 weeks ago

Built a tool for sharing images/files to my remote agents: contextdrop.dev Upload a file and get a temp link or login on both machines and let agents pull files. I liked the simplicity of @bentlegen’s glance.sh but wanted something similar as a pure CLI.

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Avyay Varadarajan @avyvar

2 months ago

@Xylon_lew @daridotdev on every new message sent we try to reconnect to the sandbox as if its warm - if e2b has unexpected behavior ie pausing sandboxes, we fallback to resuming the sandbox. if the sandbox is dead, then we rehydrate via the durable session/checkpoint.

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Avyay Varadarajan @avyvar

2 months ago

We made deploying agents to prod so easy your coding agent can do it in under 5 minutes. Get a production-grade API that scales to 100s of concurrent sessions, pauses for days between messages, and recovers sessions even on sandbox crashes or LLM provider failure. @daridotdev

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Avyay Varadarajan @avyvar

2 months ago

Get started with $10 of free credits at app.dari.dev or book a demo at calendly.com/avyay-dari.

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Avyay Varadarajan @avyvar

2 months ago

me watching bugbot review the PR that codex wrote fully

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Avyay Varadarajan @avyvar

2 months ago

"none of github's features work reliably anymore, everyone's gonna churn" github is not a car

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Avyay Varadarajan @avyvar

2 months ago

x.com/i/article/2047…

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Rick Lamers @ricklamers

2 months ago

Agent infra is undifferentiated but hard to do well, delegate to this cracked team instead and focus on your vertical’s differentiation🧠

Avyay Varadarajan @avyvar

2 months ago

Introducing @daridotdev. Deploy your AI agent as a stateful, versioned API. - durable sessions - built-in execution + sandboxes - tools, files, retries handled Stop stitching infra. Just dari deploy.

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Avyay Varadarajan @avyvar

2 months ago

Try it out at docs.dari.dev or book a call at calendly.com/avyay-dari/30m….

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Avyay Varadarajan @avyvar

2 months ago

We're intentional about letting you own your stack. You bring your own model, sandbox, and API keys. Your agent is still your code and your providers. You fully own your agent -- not just logs in someone else’s dashboard.

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Avyay Varadarajan @avyvar

2 months ago

Introducing @daridotdev. Deploy your AI agent as a stateful, versioned API. - durable sessions - built-in execution + sandboxes - tools, files, retries handled Stop stitching infra. Just dari deploy.