Ex-SpaceXAI engineer's Grok Bot tricks cut tokens 8x, plus 20 repos
A video claims a Grok Bot setup cut usage from 100,000 to 12,500 tokens per task and lists 20 GitHub repos used to lower cost and improve results.
ex-SpaceXAI engineer shared his best tricks for getting more out of Grok Bot, and this is a f*cking goldmine his Grok Bot setup went from 100,000 to 12,500 tokens per task. 8x less usage, fewer failed runs, and 10x more usable results from the same budget 20 GitHub repos he used to cut costs and improve his Grok Bot’s results: 01 JSON Repair → fix malformed JSON in code before asking the model to generate the entire response again http://github.com/mangiucugna/json_repair 02 Docling → turn PDFs and office documents into structured content, preserving layout and tables so your bot gets cleaner material to work with http://github.com/docling-project/docling 03 PyMuPDF4LLM → extract PDFs into Markdown, then select the pages and sections your task needs instead of loading the whole document http://github.com/pymupdf/pymupdf4llm 04 LanceDB → store searchable document chunks and retrieve relevant evidence for each request instead of attaching the entire archive http://github.com/lancedb/lancedb 05 FastEmbed → generate embeddings locally with supported models, reducing reliance on paid embedding calls when indexing and searching your material http://github.com/qdrant/fastembed 06 FlashRank → rerank search results before generation so your bot reads the strongest passages rather than every vaguely related match http://github.com/PrithivirajDamodaran/FlashRank 07 datasketch → detect near-duplicate text with similarity search, helping your pipeline remove repeated material before analysis http://github.com/ekzhu/datasketch 08 joblib → cache expensive Python function results on disk and reuse them when the inputs match, avoiding repeated preprocessing http://github.com/joblib/joblib 09 RQ → send background jobs to Python workers so code handles the queue while model calls happen only inside tasks that need them http://github.com/rq/rq 10 Instructor → extract answers into typed fields and validate them automatically. set a retry limit so a failed extraction stays bounded http://github.com/567-labs/instructor 11 Outlines → constrain supported model outputs to a defined structure, reducing formatting failures that trigger another generation http://github.com/dottxt-ai/outlines 12 Guardrails → apply explicit input and output checks, catching configured failures before a response moves into the next workflow step http://github.com/guardrails-ai/guardrails 13 DSPy → optimize prompts and examples against a defined quality metric, then test whether cheaper calls can meet your requirements http://github.com/stanfordnlp/dspy 14 OptiLLM → test reasoning strategies on connected models and keep the approach that delivers better results within your task budget http://github.com/algorithmicsuperintelligence/optillm 15 vLLM → serve compatible open models with batching and efficient memory management when you run your own API workers http://github.com/vllm-project/vllm 16 SGLang → run compatible self-hosted workers with optimized serving and prefix reuse for requests sharing the same starting context http://github.com/sgl-project/sglang 17 LMCache → reuse stored model attention state on compatible inference servers, reducing repeated processing of shared context http://github.com/LMCache/LMCache 18 DeepEval → test cheaper calls and revised prompts against repeatable checks before trusting them with the full workload http://github.com/confident-ai/deepeval 19 Ragas → evaluate retrieval and generated answers so shorter context doesn’t quietly turn into missing evidence or worse responses http://github.com/vibrantlabsai/ragas 20 Laminar → trace instrumented agent runs and inspect the calls behind slow, expensive, or failed tasks before deciding what to change http://github.com/lmnr-ai/lmnr connect the tools through your workflow code, terminal, or supported MCP integration. use serving tools for your own compatible workers and API tools for calls you control start with one recurring task. remove duplicate inputs, retrieve better evidence, and compare accepted results against the full cost of the run clean inputs → reusable work → checked outputs → fewer expensive repeats save this before your next upgrade the full course below shows 7 fixes to get more finished work out of the same Grok Bot plan ↓
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