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22 days ago
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Deploy to HF Spaces
Chandrakiran-088501103
24 days ago
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Route every task to a model automatically and budget AI usage in rupees
Chandra KiranUnverified signatureapi/routing.py rates each request's complexity and picks the cheapest capable tier from a model registry (DeepSeek V4 Flash, DeepSeek V4 Pro, GLM-5.3 on OpenRouter by default; MODEL_REGISTRY overrides). A draft that fails its checks is repaired one tier up, so hard tasks reach the strongest model on their own. Scenes also get a simulator choice among what can run them (MuJoCo, MuJoCo Warp, Isaac Sim), with the reason. api/budget.py keeps a ledger of every model call: OpenRouter's reported cost (else list prices), converted to rupees, per tester (125 each) and for the pool (500). OpenRouter-exempt calls are recorded but not deducted. Tasks are refused when the allocation or pool can't cover them and need confirmation above CONFIRM_ABOVE_INR. New: POST /feedback (helpful / not helpful + note, stored with routing and result context) and GET /me/usage (team view for ADMIN_TESTERS).5b063c2
26 days ago
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Track every backend job with its cost and stop at a monthly budget
Chandra KiranUnverified signatureRuns become one kind of job (sim, sweep, ros_check, review, export, isaac) in the same table, with the hardware it ran on and an estimated cost. New jobs are refused with 402 once the month's estimate reaches MONTHLY_CREDIT_LIMIT_USD (default $28). Sandboxed workers can return files inline; the API stores them in R2. GET /jobs and /jobs/{id} show any job, /report/{id} renders it with its follow-up jobs.1a36c6b -
Use GLM-5.3 via OpenRouter and run simulations on Modal (RunPod optional)
Chandra KiranUnverified signature- LLM settings are provider-neutral (LLM_API_KEY, LLM_BASE_URL, LLM_MODEL), defaulting to z-ai/glm-5.3 on OpenRouter; OpenCode gets the same endpoint as its only provider. - SIM_BACKEND selects modal (default) or runpod. worker/modal_app.py deploys the worker on a T4 (or CPU with MODAL_GPU=none) with a container cap and time limit. - Runs record backend + job_id; GPU time falls back to the worker's own timing on Modal. Existing run tables are migrated.da839c2
27 days ago
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Add GPU simulation runs: RunPod worker, R2 storage, report page
Chandra KiranUnverified signature- POST /simulate/run re-validates the scene, logs the call (tester, time) and submits a RunPod serverless job; GET /simulate/run/{id} for status; GET /simulate/report/{id} (signed link) shows the run summary, video and an embedded Foxglove replay of the MCAP - worker/: MuJoCo simulation with the robot holding its keyframe, Foxglove MCAP (/scene, /tf, /state), H.264 video via EGL with CPU fallback, upload to Cloudflare R2; Dockerfile for RunPod - Usage visibility without caps: runs table, burst warning in logs, review runs/usage commands - R2 setup CLI (CORS for Foxglove, credential check); README setup for R2, the RunPod endpoint and the spend alert5dbd0bf -
Add Stage 1 MuJoCo scene generation API
Chandra KiranUnverified signature- RAG index (Chroma) built from MuJoCo docs, the MJCF schema and the Menagerie Unitree G1 model - POST /simulate/generate: grounded prompt (robot facts read from the model, Menagerie example, schema rows, retrieved docs), GLM-4.6, sandboxed MuJoCo load + 2 s step test, repair loop on failure - Hashed API-key allowlist with add/list/revoke CLI - SQLite log of every generation, tester feedback endpoint, review CLI - Five MuJoCo test scenarios with structural checks and a runnerf850954