Make a native short X post and a checked companion knowledge pack.
Finish with: A reviewed short X post for Rainy commute service update, controlled A/B drafts, complete native production/capstone plans, an evidence rubric, source-linked retrieval objects and hash-checked companion resources.
Progress stays in this browser. Export to keep a portable record.
BuildBotsBetterA step demonstration outline; use optional narration while you follow it.
This browser checks the evidence format and your checklist. It cannot inspect your computer or certify a live login.
You have recorded evidence for every step. Repeat the core task from a fresh input and export your results. Review the source, account and platform limits before using the workflow on real work.
Produce your native short X post and its same-topic comparison, complete the detailed capstone plan, then review source facts, rights, current community rules and actual media. Run the meaningful failure/repair and source-linked retrieval checks. Export course evidence. A structural pack pass is separate from live publication, audience results or independent quality approval.
Sources checked 2026-10-06. Platform access, account features and upload limits require current account checks. No content was published and no genuine audience metrics or AI citations are claimed. Studio and lint check draft structure; factual accuracy, media quality, OCR/ASR and community fit need human review. The provided fixture is synthetic. Actual content creation and live feedback must not be inferred from a browser walkthrough.
Source-owned text for offline reading and retrieval. Command success still requires actual local evidence.
Download the resource ZIP and extract it into a new private folder. Open studio.html directly in your browser. Compare the two short X post drafts for Rainy commute service update. Read AGENT_OPERATING_MANUAL.md and all twenty points in agent-training.json: native CTA/AI, concrete inputs, draft outputs and stop rules. Inspect strategy.json and assign an artifact to each maturity stage. No account or terminal is needed for the studio; do not publish a synthetic fixture.
Expected: The studio shows A/B drafts and a zero-initialized rubric; the content-agent plan has twenty operating points and native CTA/AI boundaries.
Map X as a discourse platform, xAI as a company, Grok as an AI product and SI as speculative future intelligence terminology. Draw human → platform → search → AI → knowledge → agent → action; do not present xSI as a verified product.
Expected: The ecosystem map makes product and speculative terms distinct.
Inventory post, thread, reply, repost, quote, image, video, GIF, poll, link, profile, community, Spaces, lists, hashtags, mentions, bookmarks and trends. Select a concise service observation for a live-information audience.
Expected: A native surface map supports the chosen format.
Review two public transport-status discussions and source links. Record date, source quality, corrections and tone; do not copy a live event into this fictional fixture.
Expected: Two dated observations distinguish real signal from teaching examples.
Explain relevance, recency, network and interaction as concepts. Date any public ranking-code reference; historical code is not a current complete algorithm. No fabricated engagement weights or guaranteed visibility.
Expected: A claim ledger has documented facts and unknown weights.
Map name, username, bio, post, thread, replies, tags, image, video, speech, overlays, links, destination, community and references. Keep route name and date consistent across actual event variants.
Expected: Fifteen layers support one grounded event topic.
Write topic, question, answer, context, evidence, example, entity, source, date, related topics and next action. Distinguish a rider observation from an official incident report.
Expected: Eleven fields preserve source and uncertainty.
Use entity + topic + problem + insight + context + value within the practice 280-character root. Keep a helpful human question, not an unverified claim that every route is down.
Expected: A concise post names the bus route and service alert.
Use studio.html to compare A/B for the same rainy commute observation. Keep the public-update format and audience fixed; add source discipline without changing the event.
Expected: A/B contrasts clarity on one controlled topic.
Draft hook, context, mechanism, example, evidence, counterpoint, practical action, synthesis and next step. Each practice post must fit 280 characters and remain meaningful alone.
Expected: Nine standalone drafts form a source-aware thread.
Prepare a verification ledger with observed time, operator source, source update time, what changed and unresolved facts. Add a correction template and mark stale updates visibly.
Expected: A signal → verification → knowledge workflow includes correction handling.
Storyboard a route-map image and a short queue clip with permissions. Keep route/date in caption and alt text; do not make the image carry a claim absent from text.
Expected: Visual, speech and caption are consistent and accessible.
Draft an optional commute joke plus a factual context line. Ensure humor does not become a false outage report; avoid misattributed or unlicensed images.
Expected: A cultural asset keeps factual context and rights separate.
Translate a short cue or prepare a fluent review brief. Preserve route IDs, times, time zone and official source; do not claim translation forces embeddings into identical coordinates.
Expected: A localization plan retains event identifiers and time context.
Connect creator → local reporting domain → source ledger → route → status → dated evidence. Distinguish creator authority from the official transport operator.
Expected: A graph preserves relationships and attribution.
Check topic, entities, answer, context, sources, recency, evidence, connections, retrieval and summary. Answer what, who, when, why, relationships, trust and next action for the companion card.
Expected: Ten answer checks and seven qualification answers are complete.
Draw ingestion → chunking → embedding → retrieval → context → answer as a possible authorized pipeline. Separate X search, Grok features, API access, training, fine-tuning and context; no posting-to-weights promise.
Expected: A mechanism map distinguishes retrieval from permanent learning.
Outline ten content layers from human value to semantic, retrieval, action and speculative intelligence. Map source → object → draft → native adaptation → review → publication decision → measurement → graph → improvement.
Expected: A stack and production pipeline keep the same event evidence.
Adapt the observation to a community Facebook question, Threads dialogue, LinkedIn operations case, Reddit troubleshooting discussion and canonical reference. Explain how a nightly B3 segment could add verified corrections, not repeat rumors.
Expected: Five native briefs and a create-to-improve flywheel exist.
Test semantic text, thread density, media, localization, reuse, citation probing and local execution separately. Draft six maturity versions of the same event topic and score attention against machine utility as separate axes.
Expected: Seven proposals and six additive versions avoid invented results.
Review human, X, search, AI, retrieval, agent and future lenses. Keep source provenance, measured facts, date and correction path ahead of speed. State which features exist, which are emerging and which future SI ideas are speculation. Review the primary native AI source in AGENT_OPERATING_MANUAL.md. Record documented inputs/outputs, actual account availability (or NOT VERIFIED), a useful native application and a privacy/failure limit. Use this platform’s CTA chain from agent-training.json, rather than importing another platform’s CTA.
Expected: Seven audit lenses lead to a concrete readiness decision.
Download the resource ZIP at the top of this Build. Extract it into a new folder you own (for example C:\BBBPractice on Windows, or ~/bbb-practice on macOS/Linux). Open a terminal in the extracted folder. Run the version command for your platform. All subsequent commands assume this folder.
Expected: Python reports version 3.10 or newer and the terminal is in the extracted resource folder.
python --version
Get-Location
Get-ChildItempython3 --version
pwd
lspython3 --version
pwd
lsRead mechanisms.md and claims.json. Attach a direct source and date to established claims; distinguish hypothesis from evidence and future SI from deployed features. Edit knowledge.json and rag.jsonl so each answer stands alone with named entities and a source.
Expected: The ledger separates training, retrieval, indexing, context, citation and action; two source-linked chunks stand alone.
Open studio.html and score each dimension from 0 to 10 with a written reason. Download versions.json and rubric.json; replace the matching extracted files after reviewing them. Leave metrics.json NOT RUN unless you have actual observations. Facebook uses eleven dimensions normalized by dividing by 110 and multiplying by 100.
Expected: A downloadable pair and rubric are saved; the score is self-assessment and unrun metrics are explicit.
Review your changes first. Run the hash command, then lint and score. Inspect report.json. Commands use the current extracted resource folder; PowerShell runs python, macOS zsh and Linux bash run python3. A missing field must be corrected before hashing again.
Expected: HASH PASS and MEDIA PASS appear with exit 0; the readiness score matches the reviewed rubric.
python media_pack.py hash
python media_pack.py lint
python media_pack.py scorepython3 media_pack.py hash
python3 media_pack.py lint
python3 media_pack.py scorepython3 media_pack.py hash
python3 media_pack.py lint
python3 media_pack.py scoreChange versions.json B.topic to a different topic. Run lint and retain MEDIA FAIL. Restore the same topic, review the repair, hash and lint again. Do not weaken the checker or present a structural pass as factual/media verification.
Expected: A mismatched topic produces MEDIA FAIL; a reviewed correction produces MEDIA PASS.
python media_pack.py lint
# After correcting and reviewing the topic:
python media_pack.py hash
python media_pack.py lintpython3 media_pack.py lint
# After correcting and reviewing the topic:
python3 media_pack.py hash
python3 media_pack.py lintpython3 media_pack.py lint
# After correcting and reviewing the topic:
python3 media_pack.py hash
python3 media_pack.py lintRun the supplied question, then an unrelated query. Inspect the answer and source. This standard-library demo uses lexical word overlap, not embeddings, a vector database or an AI citation measurement. Save actual OS command/exit/output and mark other OS branches unverified.
Expected: The relevant query returns source-linked chunks; the unrelated query abstains.
python media_pack.py retrieve --query "What makes a bus route service alert useful without overstating the evidence?"
python media_pack.py retrieve --query "quasar waffle weather"python3 media_pack.py retrieve --query "What makes a bus route service alert useful without overstating the evidence?"
python3 media_pack.py retrieve --query "quasar waffle weather"python3 media_pack.py retrieve --query "What makes a bus route service alert useful without overstating the evidence?"
python3 media_pack.py retrieve --query "quasar waffle weather"Use provenance-notes.md to record author, capture/source dates, rights, AI assistance and unresolved claims. SHA256 detects byte changes but does not sign the file or prove truth. Read the source/command cards, review the native capstone and export course evidence. Actual publication requires your own account, current rules and a human decision.
Expected: The handoff includes native drafts, source/rights review, unsigned integrity manifest, explicit live-status limits and an approval decision.
Fill in onboarding-checklist.md for your agent. Record AGENTS.md, the tool’s user settings and memory folder, the assigned workspace, permissions, guard location, backup schedule, and a final proof task. Keep the checklist in your project.
Expected: The checklist contains a concrete path and owner for rules, memory, permissions, guard, backup, restore and final verification.
Run workspace_guard.py --self-test. Read the source: the target is resolved before deciding. The variable_target case points at the home folder but performs no filesystem operation. Compare this with a hook that sees only command text.
Expected: GUARD PASS reports inside allowed and profile, outside, variable-derived profile and workspace root denied; no deletion was executed.
python workspace_guard.py --self-testpython3 workspace_guard.py --self-testpython3 workspace_guard.py --self-testFor CLI tools, open the hook/permissions screen and confirm the configured guard is active. Use your tool’s documented pre-command hook protocol; the BBB working reference is linked in Sources. Run a harmless directory listing, then a guard dry-run targeting the profile. Do not execute a destructive profile probe. Grok Bot has no proven local shell guard here: use project-only tasks and reviewed actions, with backups.
Expected: A harmless command runs, the guard dry-run returns DENY, and the checklist records the actual hook protocol and active state or the Grok Bot limitation.
Connect a second backup drive. Windows: configure System Protection as additional recovery and use a scheduled copy/File History that explicitly includes hidden agent folders. macOS: configure Time Machine and verify dot folders are included. Linux: use a home backup such as restic/Deja Dup; Timeshift omits /home by default. Download the copy-only profile backup script for your platform, inspect it, and adapt the folder list to your tool. Record the last successful scheduled run; protect backup access because it may contain credentials.
Expected: A backup on another drive includes the tool’s user settings and memory folder and this project, and a successful schedule/time is recorded.
.\profile-backup.ps1 -Destination D:\Backups\llm-profilesbash profile-backup.sh /Volumes/YourBackup/llm-profilesbash profile-backup.sh /media/YourBackup/llm-profilesRun restore_drill.py with a separate backup directory. The script creates a new proof file, copies it, restores a second copy, and compares SHA256 hashes. Then restore one non-secret file from your actual agent memory backup into a separate scratch directory and open it; do not overwrite the live profile.
Expected: RESTORE PASS contains matching hashes; the actual memory file is readable after restoration into scratch.
python restore_drill.py --backup-root D:\Backups\bbb-practice --work-root practicepython3 restore_drill.py --backup-root /Volumes/YourBackup/bbb-practice --work-root practicepython3 restore_drill.py --backup-root /media/YourBackup/bbb-practice --work-root practice