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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 02 Docling → turn PDFs and office documents into structured content, preserving layout and tables so your bot gets cleaner material to work with 03 PyMuPDF4LLM → extract PDFs into Markdown, then select the pages and sections your task needs instead of loading the whole document 04 LanceDB → store searchable document chunks and retrieve relevant evidence for each request instead of attaching the entire archive 05 FastEmbed → generate embeddings locally with supported models, reducing reliance on paid embedding calls when indexing and searching your material 06 FlashRank → rerank search results before generation so your bot reads the strongest passages rather than every vaguely related match 07 datasketch → detect near-duplicate text with similarity search, helping your pipeline remove repeated material before analysis 08 joblib → cache expensive Python function results on disk and reuse them when the inputs match, avoiding repeated preprocessing 09 RQ → send background jobs to Python workers so code handles the queue while model calls happen only inside tasks that need them 10 Instructor → extract answers into typed fields and validate them automatically. set a retry limit so a failed extraction stays bounded 11 Outlines → constrain supported model outputs to a defined structure, reducing formatting failures that trigger another generation 12 Guardrails → apply explicit input and output checks, catching configured failures before a response moves into the next workflow step 13 DSPy → optimize prompts and examples against a defined quality metric, then test whether cheaper calls can meet your requirements 14 OptiLLM → test reasoning strategies on connected models and keep the approach that delivers better results within your task budget 15 vLLM → serve compatible open models with batching and efficient memory management when you run your own API workers 16 SGLang → run compatible self-hosted workers with optimized serving and prefix reuse for requests sharing the same starting context 17 LMCache → reuse stored model attention state on compatible inference servers, reducing repeated processing of shared context 18 DeepEval → test cheaper calls and revised prompts against repeatable checks before trusting them with the full workload 19 Ragas → evaluate retrieval and generated answers so shorter context doesn’t quietly turn into missing evidence or worse responses 20 Laminar → trace instrumented agent runs and inspect the calls behind slow, expensive, or failed tasks before deciding what to change 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 ↓

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 02 Docling → turn PDFs and office documents into structured content, preserving layout and tables so your bot gets cleaner material to work with 03 PyMuPDF4LLM → extract PDFs into Markdown, then select the pages and sections your task needs instead of loading the whole document 04 LanceDB → store searchable document chunks and retrieve relevant evidence for each request instead of attaching the entire archive 05 FastEmbed → generate embeddings locally with supported models, reducing reliance on paid embedding calls when indexing and searching your material 06 FlashRank → rerank search results before generation so your bot reads the strongest passages rather than every vaguely related match 07 datasketch → detect near-duplicate text with similarity search, helping your pipeline remove repeated material before analysis 08 joblib → cache expensive Python function results on disk and reuse them when the inputs match, avoiding repeated preprocessing 09 RQ → send background jobs to Python workers so code handles the queue while model calls happen only inside tasks that need them 10 Instructor → extract answers into typed fields and validate them automatically. set a retry limit so a failed extraction stays bounded 11 Outlines → constrain supported model outputs to a defined structure, reducing formatting failures that trigger another generation 12 Guardrails → apply explicit input and output checks, catching configured failures before a response moves into the next workflow step 13 DSPy → optimize prompts and examples against a defined quality metric, then test whether cheaper calls can meet your requirements 14 OptiLLM → test reasoning strategies on connected models and keep the approach that delivers better results within your task budget 15 vLLM → serve compatible open models with batching and efficient memory management when you run your own API workers 16 SGLang → run compatible self-hosted workers with optimized serving and prefix reuse for requests sharing the same starting context 17 LMCache → reuse stored model attention state on compatible inference servers, reducing repeated processing of shared context 18 DeepEval → test cheaper calls and revised prompts against repeatable checks before trusting them with the full workload 19 Ragas → evaluate retrieval and generated answers so shorter context doesn’t quietly turn into missing evidence or worse responses 20 Laminar → trace instrumented agent runs and inspect the calls behind slow, expensive, or failed tasks before deciding what to change 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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THIS IS F*CKING DANGEROUS ex-SpaceXAI employee leaked a second-brain template for Grok Bot. 99% of Grok Bot users don’t know this setup exists your documents, voice notes, research, and past decisions become connected memory. ask why you rejected an idea six months ago, and it retrieves the reasoning and sources. less repeated work, fewer wasted tokens, and answers grounded in what you already know 20 GitHub repos to recreate the setup: 01 Basic Memory → keep knowledge in readable Markdown files that an AI can search and update through MCP. your notes stay accessible outside the chat 02 Cognee → turn documents and conversations into persistent knowledge graphs, connecting decisions to the discussions and evidence behind them 03 MemOS → add a memory layer that stores, retrieves, and manages information across tasks, with feedback for correcting what the system remembers 04 Mem0 → retain useful preferences, facts, and interaction history so connected workflows can retrieve relevant memories in later sessions 05 Graphiti → track relationships and how facts change over time, preserving the difference between what was true before and what is true now 06 LightRAG → combine graph relationships and vector retrieval to find connected information across documents, with support for incremental updates 07 PageIndex → build a navigable tree of a long document so retrieval can find relevant sections with traceable page references 08 Karakeep → collect links, notes, images, and PDFs in a searchable library, with optional automatic tagging and summarization 09 Linkwarden → preserve useful web pages as archived copies, keeping the source available even when the original link disappears 10 Paperless-ngx → turn scanned documents and paperwork into an organized, searchable archive that can feed your knowledge workflow 11 Unstructured → break documents into structured elements so your ingestion pipeline can separate useful content before indexing it 12 MinerU → extract readable content from complex PDFs, scans, tables, and office documents, with references back to document locations 13 whisper.cpp → transcribe recordings locally so spoken ideas and meeting discussions can become searchable text in your archive 14 pgvector → store embeddings alongside your structured data in Postgres, then retrieve related passages with metadata and filters 15 sqlite-vec → add vector search to a lightweight SQLite database for a local memory store without a separate database server 16 Meilisearch → combine keyword and semantic search so your archive can find both an exact project name and a related idea 17 Neo4j → store explicit connections between people, projects, sources, and decisions, then query how those pieces relate 18 watchfiles → detect changed files and trigger updates in your indexing scripts, keeping the archive current without repeatedly processing everything 19 Syncthing → synchronize your knowledge files between computers so the notes you capture and the files your workflow reads stay aligned 20 restic → create encrypted backups and recover previous snapshots when a bad edit or accidental deletion damages your knowledge collection start with one project and one memory backend. connect it to Grok Bot through supported MCP tools, APIs, or local scripts, then add the capture and retrieval pieces you need test it with a question you already know the answer to: “why did we reject this approach?” a useful second brain should bring back the original reasoning, its evidence, and any later decision that replaced it that’s when an old conversation becomes something you can build on months later save this. the work you’ve already done should keep paying you back the article below shows how to reuse that knowledge without loading your entire archive into every Grok Bot conversation ↓

THIS IS F*CKING DANGEROUS ex-SpaceXAI employee leaked a second-brain template for Grok Bot. 99% of Grok Bot users don’t know this setup exists your documents, voice notes, research, and past decisions become connected memory. ask why you rejected an idea six months ago, and it retrieves the reasoning and sources. less repeated work, fewer wasted tokens, and answers grounded in what you already know 20 GitHub repos to recreate the setup: 01 Basic Memory → keep knowledge in readable Markdown files that an AI can search and update through MCP. your notes stay accessible outside the chat 02 Cognee → turn documents and conversations into persistent knowledge graphs, connecting decisions to the discussions and evidence behind them 03 MemOS → add a memory layer that stores, retrieves, and manages information across tasks, with feedback for correcting what the system remembers 04 Mem0 → retain useful preferences, facts, and interaction history so connected workflows can retrieve relevant memories in later sessions 05 Graphiti → track relationships and how facts change over time, preserving the difference between what was true before and what is true now 06 LightRAG → combine graph relationships and vector retrieval to find connected information across documents, with support for incremental updates 07 PageIndex → build a navigable tree of a long document so retrieval can find relevant sections with traceable page references 08 Karakeep → collect links, notes, images, and PDFs in a searchable library, with optional automatic tagging and summarization 09 Linkwarden → preserve useful web pages as archived copies, keeping the source available even when the original link disappears 10 Paperless-ngx → turn scanned documents and paperwork into an organized, searchable archive that can feed your knowledge workflow 11 Unstructured → break documents into structured elements so your ingestion pipeline can separate useful content before indexing it 12 MinerU → extract readable content from complex PDFs, scans, tables, and office documents, with references back to document locations 13 whisper.cpp → transcribe recordings locally so spoken ideas and meeting discussions can become searchable text in your archive 14 pgvector → store embeddings alongside your structured data in Postgres, then retrieve related passages with metadata and filters 15 sqlite-vec → add vector search to a lightweight SQLite database for a local memory store without a separate database server 16 Meilisearch → combine keyword and semantic search so your archive can find both an exact project name and a related idea 17 Neo4j → store explicit connections between people, projects, sources, and decisions, then query how those pieces relate 18 watchfiles → detect changed files and trigger updates in your indexing scripts, keeping the archive current without repeatedly processing everything 19 Syncthing → synchronize your knowledge files between computers so the notes you capture and the files your workflow reads stay aligned 20 restic → create encrypted backups and recover previous snapshots when a bad edit or accidental deletion damages your knowledge collection start with one project and one memory backend. connect it to Grok Bot through supported MCP tools, APIs, or local scripts, then add the capture and retrieval pieces you need test it with a question you already know the answer to: “why did we reject this approach?” a useful second brain should bring back the original reasoning, its evidence, and any later decision that replaced it that’s when an old conversation becomes something you can build on months later save this. the work you’ve already done should keep paying you back the article below shows how to reuse that knowledge without loading your entire archive into every Grok Bot conversation ↓

28,163 görüntüleme

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THIS IS F*CKING INSANE SpaceXAI just dropped 146 Grok Bot templates worth $29,200 for FREE bots like these basically cost $50–$200 each. and that’s just the official collection. GitHub already has 700+ bots, including complete teams with specialist roles, ready-made handoffs, and separate verifiers 20 GitHub projects you should open first: 01 Agent Org Chart → ready-made team structures for engineering, marketing, support, and sales. pick the organization your job needs instead of inventing every role 02 Grok Factory → one Firstmate routes software, research, and general work to the right specialists, then brings back reports or reviewed pull requests 03 multiBot → creates purpose-built teammates that delegate heavy work to coding CLIs, keeping Grok Bot focused on coordination and returning the result 04 Grok Bot Architecture → a blueprint for a chief of staff, specialist bots, structured handoffs, connected tools, and human approval points on one shared computer 05 Foreman → inspects a hosted bot crew, surfaces stalled work, and records evidence behind completion claims. a separate checker supplies the passing verdict 06 Grok Bot Second Brain → a five-bot setup with Conductor, Capture, Memory, Ops, and Research, sharing a knowledge vault and defined responsibilities 07 grokbot-skills → reusable skills for debugging, brainstorming, writing, and observing tasks, with upstream sources and licenses preserved 08 grokbot-x → an X content workflow that scouts posts, prepares drafts, publishes through Typefully, and uses logged results to evaluate changes to its writing skills 09 Restaurant Scout → a specialist setup for finding food brands and creators, preparing shoppable-content scripts, and organizing the findings into a catalog workflow 10 Grok Bot Shopping → shopping skills for product search, merchant checkout, Shopify discount-code tests, and order workflows, plus an optional Buyer bot 11 PhoneZero → gives Grok Bot a phone workflow for tasks like restaurant bookings. it prepares the call plan, waits for approval, and reports the outcome 12 Unlist → a privacy assistant playbook for people-search opt-outs, with site records, letter generation, submission logs, and follow-up tracking 13 Grok Bot Gateway → lets coding agents and scripts list bots, read conversations, and send tasks through a gateway bot using a webhook routine 14 grokbot-queue → queues and dispatches tasks across multiple Grok Bots, then collects their results using SSH and unofficial desktop automation 15 Thin Grok Bot → a delegation skill that keeps the bot team focused on routing while deeper builds and investigations run through coding CLIs or cloud agents 16 BOTROSTER → a separate self-hosted alternative with persistent named teammates, shared files, a browser, scheduled work, and approval controls 17 Root Agent → a coordinator that determines which specialists a request needs, manages assignments, and hands domain work to leads 18 Grok Bot Profiles → four focused roles: Spec Writer, Bug Reproducer, PR Producer, and PR Verifier. implementation and verification stay in separate conversations 19 Grok Bot Templates → 49 templates with defined jobs, outputs, skills, routines, and boundaries, plus team setups you can adapt to your work 20 HyperGrok Trading Desk → seven bots covering market data, research, strategy, risk, execution, and review. starts in research mode; live trades require your approval give the first team a job with an obvious finish line: one researched brief, one reviewed PR, or one prepared batch of drafts then check how much work you actually accepted and what it cost to produce. a larger roster earns its place when it improves that result the setup can be free. paying several bots to repeat the same investigation still costs you save this before you spend another weekend building a workflow that already exists the article below shows how to keep context smaller, reuse completed work, and contain retries so more of your Grok Bot budget reaches the finished result ↓

THIS IS F*CKING INSANE SpaceXAI just dropped 146 Grok Bot templates worth $29,200 for FREE bots like these basically cost $50–$200 each. and that’s just the official collection. GitHub already has 700+ bots, including complete teams with specialist roles, ready-made handoffs, and separate verifiers 20 GitHub projects you should open first: 01 Agent Org Chart → ready-made team structures for engineering, marketing, support, and sales. pick the organization your job needs instead of inventing every role 02 Grok Factory → one Firstmate routes software, research, and general work to the right specialists, then brings back reports or reviewed pull requests 03 multiBot → creates purpose-built teammates that delegate heavy work to coding CLIs, keeping Grok Bot focused on coordination and returning the result 04 Grok Bot Architecture → a blueprint for a chief of staff, specialist bots, structured handoffs, connected tools, and human approval points on one shared computer 05 Foreman → inspects a hosted bot crew, surfaces stalled work, and records evidence behind completion claims. a separate checker supplies the passing verdict 06 Grok Bot Second Brain → a five-bot setup with Conductor, Capture, Memory, Ops, and Research, sharing a knowledge vault and defined responsibilities 07 grokbot-skills → reusable skills for debugging, brainstorming, writing, and observing tasks, with upstream sources and licenses preserved 08 grokbot-x → an X content workflow that scouts posts, prepares drafts, publishes through Typefully, and uses logged results to evaluate changes to its writing skills 09 Restaurant Scout → a specialist setup for finding food brands and creators, preparing shoppable-content scripts, and organizing the findings into a catalog workflow 10 Grok Bot Shopping → shopping skills for product search, merchant checkout, Shopify discount-code tests, and order workflows, plus an optional Buyer bot 11 PhoneZero → gives Grok Bot a phone workflow for tasks like restaurant bookings. it prepares the call plan, waits for approval, and reports the outcome 12 Unlist → a privacy assistant playbook for people-search opt-outs, with site records, letter generation, submission logs, and follow-up tracking 13 Grok Bot Gateway → lets coding agents and scripts list bots, read conversations, and send tasks through a gateway bot using a webhook routine 14 grokbot-queue → queues and dispatches tasks across multiple Grok Bots, then collects their results using SSH and unofficial desktop automation 15 Thin Grok Bot → a delegation skill that keeps the bot team focused on routing while deeper builds and investigations run through coding CLIs or cloud agents 16 BOTROSTER → a separate self-hosted alternative with persistent named teammates, shared files, a browser, scheduled work, and approval controls 17 Root Agent → a coordinator that determines which specialists a request needs, manages assignments, and hands domain work to leads 18 Grok Bot Profiles → four focused roles: Spec Writer, Bug Reproducer, PR Producer, and PR Verifier. implementation and verification stay in separate conversations 19 Grok Bot Templates → 49 templates with defined jobs, outputs, skills, routines, and boundaries, plus team setups you can adapt to your work 20 HyperGrok Trading Desk → seven bots covering market data, research, strategy, risk, execution, and review. starts in research mode; live trades require your approval give the first team a job with an obvious finish line: one researched brief, one reviewed PR, or one prepared batch of drafts then check how much work you actually accepted and what it cost to produce. a larger roster earns its place when it improves that result the setup can be free. paying several bots to repeat the same investigation still costs you save this before you spend another weekend building a workflow that already exists the article below shows how to keep context smaller, reuse completed work, and contain retries so more of your Grok Bot budget reaches the finished result ↓

323,122 görüntüleme • 2 gün önce

THIS IS A PURE F*CKING GOLD beyond SpaceXAI’s 146 official templates for Grok Bot, there are another 624+ ready-made bots you can use completely FREE. and almost nobody knows they exist full teams with specialist roles, bots that hand work to each other, separate verifiers that check the output, and entire multi-bot trading desks. all open source 20 GitHub projects worth opening first: 01 awesome-grokbot → a searchable index of public Grok Bot configurations, organized by job. find an existing setup instead of writing every instruction yourself 02 botteams → pick a company team and copy its installer prompt. it creates named bots, a group chat, and proposes routines for your approval 03 rosterroom → 82 paste-ready team setups covering founders, operations, security, finance, and research. each includes roles, a first assignment, and approval boundaries 04 grok-bot-profiles → Spec Writer, Bug Reproducer, PR Producer, and PR Verifier. the verifier works in a separate conversation so the builder doesn’t approve its own work 05 hypergrok-trading-desk → 7 specialist bots and 17 skills for market research, strategy testing, risk, execution, and review. starts with public data; trades require your approval 06 root-agent-skill-framework → a coordinator that sizes the team your request needs, creates specialists, and delegates daily management to leads 07 awesome-grokbot by mergisi → paste START.md into a setup bot and choose an engineering, sales, marketing, success, operations, or personal team 08 grok-bot-templates → 49 templates, 10 teams, 47 skills, and 8 routines. each setup defines the job, deliverable, boundaries, and first task 09 botskills → paste-ready BOT.md setups with required integrations and a clear boundary: the action each bot must never take without you 10 grok-skills → 195 reusable playbooks for code review, debugging, research, writing, planning, and shipping. add a proven process instead of another vague instruction 11 crew-contract → a working agreement for bot teams: defined missions, named owners, evidence in handoffs, completion checks, and stopping when the budget runs out 12 grokbot-for-gtm → an outbound playbook that connects prospect research, email, LinkedIn, replies, and meeting tracking. bring your accounts and approve the sends 13 gojiberryai-sales-os → 13 sales roles covering buying signals, prospect research, enrichment, copy, follow-ups, qualification, and pipeline analysis through the GojiberryAI MCP 14 grok-research → an equity research team with persistent researchers, scout reports, reviewed views, and a shared research database. research stays separate from live trading 15 grok-bot-token-saver → a chief-and-workers setup that replaces dragging old conversations forward with short role cards and handoffs for the next session 16 grok-bot-cli → create bots and groups, send messages, and inspect threads from scripts. it reuses your signed-in desktop session without manual token copying 17 grok-bot-skill → let a coding agent list, message, and create Grok Bot teammates, connecting development work with the bots already running your workflows 18 awesome-grok-bot by majiayu000 → another searchable catalog of public bot shares, plus team recipes, setup guides, and community tools organized by job 19 akeru-bot → a separate, self-hosted alternative with named teammates, per-bot tools, instructions, and memory. keep profiles and conversation history on infrastructure you control 20 guaca → a separate desktop workspace where agents share conversations, delegate tasks, and use MCP tools. run the workspace locally or on your own server start with one job you already understand. read the setup, connect the tools it needs, and inspect the first result before scheduling it the templates and source code are free. running them still uses your plan, APIs, or hosting then make the team share findings instead of repeating research. keep handoffs short, preserve completed work, and give failed operations a stopping point save this. your next useful bot might already be built the article below breaks down 7 workflow changes to make that setup cheaper and get more finished work from the same budget ↓

THIS IS A PURE F*CKING GOLD beyond SpaceXAI’s 146 official templates for Grok Bot, there are another 624+ ready-made bots you can use completely FREE. and almost nobody knows they exist full teams with specialist roles, bots that hand work to each other, separate verifiers that check the output, and entire multi-bot trading desks. all open source 20 GitHub projects worth opening first: 01 awesome-grokbot → a searchable index of public Grok Bot configurations, organized by job. find an existing setup instead of writing every instruction yourself 02 botteams → pick a company team and copy its installer prompt. it creates named bots, a group chat, and proposes routines for your approval 03 rosterroom → 82 paste-ready team setups covering founders, operations, security, finance, and research. each includes roles, a first assignment, and approval boundaries 04 grok-bot-profiles → Spec Writer, Bug Reproducer, PR Producer, and PR Verifier. the verifier works in a separate conversation so the builder doesn’t approve its own work 05 hypergrok-trading-desk → 7 specialist bots and 17 skills for market research, strategy testing, risk, execution, and review. starts with public data; trades require your approval 06 root-agent-skill-framework → a coordinator that sizes the team your request needs, creates specialists, and delegates daily management to leads 07 awesome-grokbot by mergisi → paste START.md into a setup bot and choose an engineering, sales, marketing, success, operations, or personal team 08 grok-bot-templates → 49 templates, 10 teams, 47 skills, and 8 routines. each setup defines the job, deliverable, boundaries, and first task 09 botskills → paste-ready BOT.md setups with required integrations and a clear boundary: the action each bot must never take without you 10 grok-skills → 195 reusable playbooks for code review, debugging, research, writing, planning, and shipping. add a proven process instead of another vague instruction 11 crew-contract → a working agreement for bot teams: defined missions, named owners, evidence in handoffs, completion checks, and stopping when the budget runs out 12 grokbot-for-gtm → an outbound playbook that connects prospect research, email, LinkedIn, replies, and meeting tracking. bring your accounts and approve the sends 13 gojiberryai-sales-os → 13 sales roles covering buying signals, prospect research, enrichment, copy, follow-ups, qualification, and pipeline analysis through the GojiberryAI MCP 14 grok-research → an equity research team with persistent researchers, scout reports, reviewed views, and a shared research database. research stays separate from live trading 15 grok-bot-token-saver → a chief-and-workers setup that replaces dragging old conversations forward with short role cards and handoffs for the next session 16 grok-bot-cli → create bots and groups, send messages, and inspect threads from scripts. it reuses your signed-in desktop session without manual token copying 17 grok-bot-skill → let a coding agent list, message, and create Grok Bot teammates, connecting development work with the bots already running your workflows 18 awesome-grok-bot by majiayu000 → another searchable catalog of public bot shares, plus team recipes, setup guides, and community tools organized by job 19 akeru-bot → a separate, self-hosted alternative with named teammates, per-bot tools, instructions, and memory. keep profiles and conversation history on infrastructure you control 20 guaca → a separate desktop workspace where agents share conversations, delegate tasks, and use MCP tools. run the workspace locally or on your own server start with one job you already understand. read the setup, connect the tools it needs, and inspect the first result before scheduling it the templates and source code are free. running them still uses your plan, APIs, or hosting then make the team share findings instead of repeating research. keep handoffs short, preserve completed work, and give failed operations a stopping point save this. your next useful bot might already be built the article below breaks down 7 workflow changes to make that setup cheaper and get more finished work from the same budget ↓

99,168 görüntüleme • 3 gün önce

YOUR OLD GROK BOT SETUP IS THE PROBLEM SpaceXAI just admitted the old rules you gave Grok Bot are making it pricier and dumber. 75% of your tokens burned on pointless work. a $200 plan delivering $50 worth of actual results 20 GitHub repos to fix the setup that’s wasting your tokens and dragging down your results: 01 Promptfoo → compare your current instructions against a cleaner version on the same test cases, so removing a rule becomes something you can evaluate 02 DSPy → optimize instructions and examples against a defined quality metric, replacing endless manual prompt edits with measured experiments 03 SAMMO → experiment with prompt structure, wording, and compression while checking whether the task still gets done correctly 04 PromptWizard → generate, critique, and refine candidate instructions using task examples, then test the revised prompt before adopting it 05 DeepEval → turn recurring failures into repeatable evaluations so a shorter prompt doesn’t quietly lose requirements your workflow depends on 06 Inspect AI → evaluate complete interactions, including tool use and multi-turn behavior, to catch instructions that send the workflow down unnecessary paths 07 Langfuse → trace instrumented runs and track prompt versions, helping you connect a configuration change to repeated calls or declining output quality 08 OpenLIT → monitor usage, cost, and latency in connected AI workflows so you can identify expensive steps before rewriting the whole setup 09 OpenLLMetry → add tracing to supported model libraries and frameworks, making the sequence of calls behind an expensive result easier to inspect 10 Helicone → log and analyze API requests to find costly patterns, failed calls, and changes in spending across your instrumented workflow 11 Pydantic AI → enforce request, token, and tool-call limits in Python workflows so a written instruction isn’t your only control over repeated actions 12 LangGraph → preserve workflow state through checkpoints, allowing a failed step to resume without rebuilding everything that already succeeded 13 Tenacity → put retry counts and stopping conditions in your Python tools instead of relying on another paragraph telling the bot when to give up 14 jsonschema → check required fields, types, and output structure in code, reducing the need for repeated model calls to inspect predictable formatting 15 JMESPath → extract the fields your task needs from JSON responses before the model reads them, keeping irrelevant API data out of the conversation 16 yq → inspect and transform YAML, JSON, and other configuration files so you can isolate relevant settings during an audit 17 DiskCache → store reusable script results on disk so recurring jobs can reuse unchanged work across runs 18 LLMLingua → compress lengthy reference material before it enters the model’s context, then check that the details your task needs survived 19 Git → version your prompts and configuration files so you can identify when a harmful rule appeared and restore a working setup 20 delta → inspect readable diffs of instruction and configuration changes, making small additions easier to spot before comparing their effect on results rules like “verify twice” and “be maximally thorough” can turn a simple task into unnecessary checks. conflicting instructions, stale examples, and rigid procedures add more work before the bot even reaches the answer audit the instructions, skills, routines, and tool settings your workflow actually uses. locate each questionable rule, explain its likely effect, and propose a specific change test one edit on the same recurring job. preserve real requirements, quality checks, and approval boundaries. keep the revision when it reduces usage without making the result worse save this. the most expensive line in your setup might be the one you forgot you added the full guide below shows how to shrink working context, contain retries, and turn the saved budget into more completed work ↓

YOUR OLD GROK BOT SETUP IS THE PROBLEM SpaceXAI just admitted the old rules you gave Grok Bot are making it pricier and dumber. 75% of your tokens burned on pointless work. a $200 plan delivering $50 worth of actual results 20 GitHub repos to fix the setup that’s wasting your tokens and dragging down your results: 01 Promptfoo → compare your current instructions against a cleaner version on the same test cases, so removing a rule becomes something you can evaluate 02 DSPy → optimize instructions and examples against a defined quality metric, replacing endless manual prompt edits with measured experiments 03 SAMMO → experiment with prompt structure, wording, and compression while checking whether the task still gets done correctly 04 PromptWizard → generate, critique, and refine candidate instructions using task examples, then test the revised prompt before adopting it 05 DeepEval → turn recurring failures into repeatable evaluations so a shorter prompt doesn’t quietly lose requirements your workflow depends on 06 Inspect AI → evaluate complete interactions, including tool use and multi-turn behavior, to catch instructions that send the workflow down unnecessary paths 07 Langfuse → trace instrumented runs and track prompt versions, helping you connect a configuration change to repeated calls or declining output quality 08 OpenLIT → monitor usage, cost, and latency in connected AI workflows so you can identify expensive steps before rewriting the whole setup 09 OpenLLMetry → add tracing to supported model libraries and frameworks, making the sequence of calls behind an expensive result easier to inspect 10 Helicone → log and analyze API requests to find costly patterns, failed calls, and changes in spending across your instrumented workflow 11 Pydantic AI → enforce request, token, and tool-call limits in Python workflows so a written instruction isn’t your only control over repeated actions 12 LangGraph → preserve workflow state through checkpoints, allowing a failed step to resume without rebuilding everything that already succeeded 13 Tenacity → put retry counts and stopping conditions in your Python tools instead of relying on another paragraph telling the bot when to give up 14 jsonschema → check required fields, types, and output structure in code, reducing the need for repeated model calls to inspect predictable formatting 15 JMESPath → extract the fields your task needs from JSON responses before the model reads them, keeping irrelevant API data out of the conversation 16 yq → inspect and transform YAML, JSON, and other configuration files so you can isolate relevant settings during an audit 17 DiskCache → store reusable script results on disk so recurring jobs can reuse unchanged work across runs 18 LLMLingua → compress lengthy reference material before it enters the model’s context, then check that the details your task needs survived 19 Git → version your prompts and configuration files so you can identify when a harmful rule appeared and restore a working setup 20 delta → inspect readable diffs of instruction and configuration changes, making small additions easier to spot before comparing their effect on results rules like “verify twice” and “be maximally thorough” can turn a simple task into unnecessary checks. conflicting instructions, stale examples, and rigid procedures add more work before the bot even reaches the answer audit the instructions, skills, routines, and tool settings your workflow actually uses. locate each questionable rule, explain its likely effect, and propose a specific change test one edit on the same recurring job. preserve real requirements, quality checks, and approval boundaries. keep the revision when it reduces usage without making the result worse save this. the most expensive line in your setup might be the one you forgot you added the full guide below shows how to shrink working context, contain retries, and turn the saved budget into more completed work ↓

58,616 görüntüleme • 3 gün önce

holy sh*t. a 17-year-old computer science student from China runs 300 Grok Bots for less than $0.02 per bot a week the same swarm would cost $4,500 a week. he cut empty checks, stopped rereading entire chats, and killed retry loops. most bots now wake up only when there’s actual work to do 20 GitHub repos he used to make the setup cheaper: 01 LiteLLM → track spending and set budgets for connected API calls before one runaway workflow eats the whole allowance 02 RouteLLM → route simpler API requests to a cheaper model and reserve the stronger one for work that needs it 03 Semantic Router → match requests to predefined workflows using local embeddings instead of paying a large model to decide every next step 04 GPTCache → reuse suitable answers to similar requests instead of generating them again. define matching and freshness rules around the task 05 requests-cache → keep fetched responses with expiry rules, then let your scripts reuse valid data before asking the bot to investigate again 06 Hishel → add HTTP caching and revalidation to your fetching tools, keeping unchanged responses out of repeated download-and-analysis cycles 07 Crawl4AI → turn web pages into clean Markdown before analysis, reducing the navigation menus and page clutter your bot has to read 08 Firecrawl → collect web content as Markdown or structured data so your workflow can select the fields it actually needs 09 Chonkie → split long documents into manageable chunks so retrieval can select relevant passages instead of attaching the whole document 10 FlagEmbedding → rerank retrieved passages before generation, keeping the strongest evidence and dropping irrelevant matches from the prompt 11 LLMLingua → compress lengthy inputs before the model reads them. preserve the facts, constraints, and qualifications your task depends on 12 RTK → compress common command outputs before they enter the conversation, keeping repetitive logs from swallowing the useful context 13 Mem0 → store and retrieve relevant facts across runs so each assignment can start with selected memory instead of the entire conversation 14 Graphiti → keep changing facts and relationships in a searchable graph, then retrieve the relevant context instead of replaying the event history 15 Prefect → cache successful task results and reuse them when the configured inputs match, avoiding unnecessary reruns of completed workflow steps 16 Celery → put background jobs on a worker queue so code handles waiting and dispatch while model calls happen inside the tasks that need them 17 APScheduler → schedule lightweight checks in Python and batch nonurgent work before handing useful updates to the agent 18 PyBreaker → pause calls to a repeatedly failing service after a configured threshold, stopping one broken dependency from generating endless attempts 19 Portkey Gateway → cache matching API responses behind a shared gateway, letting connected workflows reuse answers across repeated requests 20 Phoenix → trace your instrumented AI workflow, inspect repeated calls, and compare changes against output quality before keeping an “optimization” connect the tools your workflow needs through the terminal, MCP, or your own code. route API calls through the gateway or router you choose so its controls actually apply start with the biggest leak: oversized inputs, repeated research, empty checks, or failed operations that keep restarting filter before reading → reuse before generating → check for changes → stop failed loops then measure completed work against total usage, including the tools running around your bot save this before you give the same waste a bigger budget the full course below shows how to put the workflow together ↓

holy sh*t. a 17-year-old computer science student from China runs 300 Grok Bots for less than $0.02 per bot a week the same swarm would cost $4,500 a week. he cut empty checks, stopped rereading entire chats, and killed retry loops. most bots now wake up only when there’s actual work to do 20 GitHub repos he used to make the setup cheaper: 01 LiteLLM → track spending and set budgets for connected API calls before one runaway workflow eats the whole allowance 02 RouteLLM → route simpler API requests to a cheaper model and reserve the stronger one for work that needs it 03 Semantic Router → match requests to predefined workflows using local embeddings instead of paying a large model to decide every next step 04 GPTCache → reuse suitable answers to similar requests instead of generating them again. define matching and freshness rules around the task 05 requests-cache → keep fetched responses with expiry rules, then let your scripts reuse valid data before asking the bot to investigate again 06 Hishel → add HTTP caching and revalidation to your fetching tools, keeping unchanged responses out of repeated download-and-analysis cycles 07 Crawl4AI → turn web pages into clean Markdown before analysis, reducing the navigation menus and page clutter your bot has to read 08 Firecrawl → collect web content as Markdown or structured data so your workflow can select the fields it actually needs 09 Chonkie → split long documents into manageable chunks so retrieval can select relevant passages instead of attaching the whole document 10 FlagEmbedding → rerank retrieved passages before generation, keeping the strongest evidence and dropping irrelevant matches from the prompt 11 LLMLingua → compress lengthy inputs before the model reads them. preserve the facts, constraints, and qualifications your task depends on 12 RTK → compress common command outputs before they enter the conversation, keeping repetitive logs from swallowing the useful context 13 Mem0 → store and retrieve relevant facts across runs so each assignment can start with selected memory instead of the entire conversation 14 Graphiti → keep changing facts and relationships in a searchable graph, then retrieve the relevant context instead of replaying the event history 15 Prefect → cache successful task results and reuse them when the configured inputs match, avoiding unnecessary reruns of completed workflow steps 16 Celery → put background jobs on a worker queue so code handles waiting and dispatch while model calls happen inside the tasks that need them 17 APScheduler → schedule lightweight checks in Python and batch nonurgent work before handing useful updates to the agent 18 PyBreaker → pause calls to a repeatedly failing service after a configured threshold, stopping one broken dependency from generating endless attempts 19 Portkey Gateway → cache matching API responses behind a shared gateway, letting connected workflows reuse answers across repeated requests 20 Phoenix → trace your instrumented AI workflow, inspect repeated calls, and compare changes against output quality before keeping an “optimization” connect the tools your workflow needs through the terminal, MCP, or your own code. route API calls through the gateway or router you choose so its controls actually apply start with the biggest leak: oversized inputs, repeated research, empty checks, or failed operations that keep restarting filter before reading → reuse before generating → check for changes → stop failed loops then measure completed work against total usage, including the tools running around your bot save this before you give the same waste a bigger budget the full course below shows how to put the workflow together ↓

71,520 görüntüleme • 6 gün önce

i genuinely don’t understand why people pay $200 for Grok Bot to burn a WEEK of usage in ONE day a bigger plan won’t fix bloated chats, repeated tool calls, or routines checking the same sources all day. it just gives the same waste a bigger budget 20 GitHub repos to stop wasting tokens and get more actual work out of your Grok Bot plan 01 Grok Bot Setup → keep working chats short, save task handoffs in files, and replace unnecessary polling with event-based workflows 02 RTK → compress command output before your bot reads it, so long logs don’t flood the conversation 03 QMD → search your notes and documents locally, then retrieve the relevant passages instead of loading entire folders 04 GBrain → store selected facts outside the chat. includes an experimental Grok Bot integration guide that needs testing on your account 05 MarkItDown → convert PDFs, spreadsheets, and office documents into Markdown, then pass only the sections your task needs 06 LLMLingua → compress lengthy input text before sending it to the model. check that important details survive the compression 07 Trafilatura → extract useful text and metadata from web pages before handing the material to your bot 08 Mozilla Readability → pull the main article out of a page instead of feeding your workflow navigation menus and surrounding clutter 09 ripgrep → find matching lines across files before your bot starts opening documents one by one 10 jq → filter API responses down to the fields you need instead of dumping huge JSON payloads into context 11 DuckDB → calculate totals, filter records, and aggregate data in SQL. give your bot the result instead of thousands of rows 12 Qdrant → build a retrieval layer that selects relevant document chunks for each request instead of attaching the whole knowledge base 13 DiskCache → cache reusable results on disk so your connected scripts can avoid repeating unchanged work across runs 14 cachetools → add expiry rules to cached Python lookups, reusing recent results while refreshing data when it becomes stale 15 → monitor pages outside the agent and send change notifications into your workflow, reducing pointless “anything new?” checks 16 n8n → handle fetching, filtering, and routing with automation steps, bringing the model in when the workflow needs judgment 17 Temporal → build durable workflows that preserve progress through failures, reducing the need to restart an entire multi-step job 18 Tenacity → set retry limits and stopping conditions in your Python tools so one failing operation doesn’t keep repeating indefinitely 19 Pydantic → validate required fields and data types in code, returning specific errors instead of asking the model to inspect every response 20 Langfuse → trace calls and token usage in instrumented API workflows, then identify the steps generating expensive repeats start with the problem consuming your allowance. pick the tool that addresses it and connect it through the terminal, MCP, or your own workflow code these are building blocks to configure around Grok Bot. measure their effect on one recurring task before adding more smaller inputs → fewer repeated calls → bounded retries → more completed work save this before your next upgrade

i genuinely don’t understand why people pay $200 for Grok Bot to burn a WEEK of usage in ONE day a bigger plan won’t fix bloated chats, repeated tool calls, or routines checking the same sources all day. it just gives the same waste a bigger budget 20 GitHub repos to stop wasting tokens and get more actual work out of your Grok Bot plan 01 Grok Bot Setup → keep working chats short, save task handoffs in files, and replace unnecessary polling with event-based workflows 02 RTK → compress command output before your bot reads it, so long logs don’t flood the conversation 03 QMD → search your notes and documents locally, then retrieve the relevant passages instead of loading entire folders 04 GBrain → store selected facts outside the chat. includes an experimental Grok Bot integration guide that needs testing on your account 05 MarkItDown → convert PDFs, spreadsheets, and office documents into Markdown, then pass only the sections your task needs 06 LLMLingua → compress lengthy input text before sending it to the model. check that important details survive the compression 07 Trafilatura → extract useful text and metadata from web pages before handing the material to your bot 08 Mozilla Readability → pull the main article out of a page instead of feeding your workflow navigation menus and surrounding clutter 09 ripgrep → find matching lines across files before your bot starts opening documents one by one 10 jq → filter API responses down to the fields you need instead of dumping huge JSON payloads into context 11 DuckDB → calculate totals, filter records, and aggregate data in SQL. give your bot the result instead of thousands of rows 12 Qdrant → build a retrieval layer that selects relevant document chunks for each request instead of attaching the whole knowledge base 13 DiskCache → cache reusable results on disk so your connected scripts can avoid repeating unchanged work across runs 14 cachetools → add expiry rules to cached Python lookups, reusing recent results while refreshing data when it becomes stale 15 → monitor pages outside the agent and send change notifications into your workflow, reducing pointless “anything new?” checks 16 n8n → handle fetching, filtering, and routing with automation steps, bringing the model in when the workflow needs judgment 17 Temporal → build durable workflows that preserve progress through failures, reducing the need to restart an entire multi-step job 18 Tenacity → set retry limits and stopping conditions in your Python tools so one failing operation doesn’t keep repeating indefinitely 19 Pydantic → validate required fields and data types in code, returning specific errors instead of asking the model to inspect every response 20 Langfuse → trace calls and token usage in instrumented API workflows, then identify the steps generating expensive repeats start with the problem consuming your allowance. pick the tool that addresses it and connect it through the terminal, MCP, or your own workflow code these are building blocks to configure around Grok Bot. measure their effect on one recurring task before adding more smaller inputs → fewer repeated calls → bounded retries → more completed work save this before your next upgrade

38,134 görüntüleme • 7 gün önce

Daha fazla içerik yok.