
Tech Expert & Content Creator | AI & Open Source | Ambassador @cognition | DMs open for collabs. 📧 [email protected] | Support me: https://xitter.cf/t.co/d6vXO8ICda
Joined October 2025
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Since a lot of you asked, let me explain what I meant.
AI models today need insane amounts of data and compute to learn language, and I think a big part of that cost comes from training them on languages that hide their own structure, like English. Chinese has the same problem, which is why US and Chinese labs are stuck in the same race for more data and bigger clusters. English is full of irregular forms like go/went and mouse/mice that follow no rule and have to be memorized. Its spelling doesn't match its sound, as in through, though, and tough. Related meanings share no visible link, so write, book, and office look like strangers. Relationships between words are hidden in word order, where moving one word can flip the meaning. Phrasal verbs like get up, get over, and get by can't be understood from their parts, words like set and run carry dozens of meanings the model has to guess between, and the history is broken, since Old English is unreadable today.
The fix is to train the core model on a language where the structure is explicit, so it learns more from less, and then add other languages on top so it can speak with anyone. That language is fully vowelized Arabic. Its root system means kataba, kaatib, maktoob, and maktaba all grow from one root, so the model learns a whole family of meanings at once. Its regular patterns let the model infer words it has never seen. Case endings mark every word's role on the word itself, so relationships are written instead of guessed, and because meaning holds when word order changes, the model learns meaning rather than position. New words are derived by clear rules instead of random borrowing. Texts from the 7th century are still readable today, giving 1,400 years of continuity, and they come with an annotated heritage of commentaries, lexicons, and formal reasoning methods, meaning the model gets the text plus how to understand it.
Nobody has seriously tested this yet. Personally, I'm aiming to train a model this way once I have the resources.
Always happy when my work helps someone.
This grokbot template kicks ass ngl...
x.ai/bot/jjoEfC1AKbHinHoqitP…
Reclaimed ~333 GB of my disk, exactly 33.3%:
Thank you @Da7_Tech!
Updated the grokbot template with all lessons learned tonight. I may be adding more to it as well that help with fleet management of storage across devices, such that your CoS can vouch for whether a working directory is needed still or not.
Da7em retweeted
This grokbot template kicks ass ngl...
x.ai/bot/jjoEfC1AKbHinHoqitP…
Reclaimed ~333 GB of my disk, exactly 33.3%:
Thank you @Da7_Tech!
Updated the grokbot template with all lessons learned tonight. I may be adding more to it as well that help with fleet management of storage across devices, such that your CoS can vouch for whether a working directory is needed still or not.
I'm running this amazing STORAGE RESCUE routine today from an @bot grokbot (which uses Opus 5.5 under the hood), so thought I'd share the template, with all MIT License credit to @Da7_Tech, whom you should be following if you are not already.
Grokbot Template:
x.ai/bot/jjoEfC1AKbHinHoqitP…
CC: @BotDirectoryAI
Starting Situation: 985GB out of 995GB (10GB Free)
Likely many hundreds of local repos and local model weights taking it up.
Da7em retweeted
I'm running this amazing STORAGE RESCUE routine today from an @bot grokbot (which uses Opus 5.5 under the hood), so thought I'd share the template, with all MIT License credit to @Da7_Tech, whom you should be following if you are not already.
Grokbot Template:
x.ai/bot/jjoEfC1AKbHinHoqitP…
CC: @BotDirectoryAI
Starting Situation: 985GB out of 995GB (10GB Free)
Likely many hundreds of local repos and local model weights taking it up.
My Mac was down to 26 GB free. The space wasn't in caches. It was in old projects, AI models, recordings, backups, and chat histories.
So I wrote a skill that teaches AI agents to free it up without losing anything.
Storage Rescue has two modes:
1. Offload. Big files go to an external drive and get checked by SHA-256 twice. Only then is the Mac copy deleted. Works with APFS, exFAT, FAT32, and NTFS.
2. Reclaim. No drive. Caches are cleared with each tool's own command, and your files go to the Trash first.
The agent surveys the disk first, then asks you what to do with each group: move, delete, or keep. Nothing happens without a clear yes.
On my Mac: 968 GB used before, 366 GB after.
Free and open, plain text, no runtime. macOS only. Works with Claude Code, Codex, Cursor, Factory Droid, Devin, Hermes.
github.com/Da7-Tech/storage-…
Anthropic is going all out.
Without a doubt, this will be the real competitor to Grok Bot.








