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Asteri

@Asteri_eth • 2,042 subscribers

AI writer | the best vibe and fine content creator, as well as shitposts | community outreach @blockstards 🌌

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PEOPLE ARE BURNING THROUGH THEIR TOKENS IN 30 MINUTES AND BLAMING CLAUDE The problem isn’t with the model. The problem is how they’re using it A simple formatting question they set Max effort The 40th message in the chat the model re-reads the entire history from the beginning Every single time. Using your tokens Low effort costs 10 times less than Max Just get into the habit of routing tasks by level and your limit won’t run out by noon Tokens don’t disappear. You’re just paying for unnecessary processing

PEOPLE ARE BURNING THROUGH THEIR TOKENS IN 30 MINUTES AND BLAMING CLAUDE The problem isn’t with the model. The problem is how they’re using it A simple formatting question they set Max effort The 40th message in the chat the model re-reads the entire history from the beginning Every single time. Using your tokens Low effort costs 10 times less than Max Just get into the habit of routing tasks by level and your limit won’t run out by noon Tokens don’t disappear. You’re just paying for unnecessary processing

15,752 views

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Karpathy found a way to reduce token consumption by 90% The problem is that the LLM re-reads the same files over and over again, loses context between documents, and provides less accurate answers as a result The solution is called Wiki Layer the LLM cleans, structures, and links all your data once, after which it never works with raw files again Three folders `raw/` for originals, `wiki/` for a clean knowledge base in Markdown, and files with rules for the agent Result up to 90% token savings on repeat queries, automatic links between documents, and a visual knowledge graph in Obsidian Everything stays on your local machine nothing goes to the cloud

Karpathy found a way to reduce token consumption by 90% The problem is that the LLM re-reads the same files over and over again, loses context between documents, and provides less accurate answers as a result The solution is called Wiki Layer the LLM cleans, structures, and links all your data once, after which it never works with raw files again Three folders `raw/` for originals, `wiki/` for a clean knowledge base in Markdown, and files with rules for the agent Result up to 90% token savings on repeat queries, automatic links between documents, and a visual knowledge graph in Obsidian Everything stays on your local machine nothing goes to the cloud

1,072,550 views • 24 days ago

NVIDIA will refund your cloud bill if you let them put a $250,000 supercomputer on your desk for $2,999 The $2,999 DGX Spark puts a 128GB AI supercomputer on your desk the same 70B models you rented in the cloud, but nothing goes over the network and no ToS governs the machine you own $22,000 in annual savings is the big number everyone posts The quiet number is even bigger it’s the contracts you’ve stopped losing due to data residency Owning your own compute is no longer just budget optimization it’s a way to sell to customers who can’t send their data to the cloud

NVIDIA will refund your cloud bill if you let them put a $250,000 supercomputer on your desk for $2,999 The $2,999 DGX Spark puts a 128GB AI supercomputer on your desk the same 70B models you rented in the cloud, but nothing goes over the network and no ToS governs the machine you own $22,000 in annual savings is the big number everyone posts The quiet number is even bigger it’s the contracts you’ve stopped losing due to data residency Owning your own compute is no longer just budget optimization it’s a way to sell to customers who can’t send their data to the cloud

143,320 views • 21 days ago

ONE AI AGENT CAN TURN EMPTY BACKYARDS INTO POOL CONTRACTOR LEADS BEFORE THE HOMEOWNER EVEN ASKS FOR A QUOTE It works like this: AI scans expensive homes, finds large backyards without pools, generates a realistic pool preview, estimates the potential value increase, and turns it into a personalized sales pitch. It’s a fully automated workflow that: > Finds homes worth $600K-$2M > Detects large unused backyards with no pool > Generates a realistic before/after pool visualization > Estimates project cost and potential home value upside > Turns everything into a ready-to-send offer for the homeowner or contractor The valuable part is not that AI can make a pretty image. It’s that the buyer sees the outcome before anyone sells it to them. That changes the whole offer. You are not saying "we build pools." You are showing the exact pool that could exist in their backyard. And in the article below you’ll find 4 more AI automations that can turn boring workflows into real revenue

ONE AI AGENT CAN TURN EMPTY BACKYARDS INTO POOL CONTRACTOR LEADS BEFORE THE HOMEOWNER EVEN ASKS FOR A QUOTE It works like this: AI scans expensive homes, finds large backyards without pools, generates a realistic pool preview, estimates the potential value increase, and turns it into a personalized sales pitch. It’s a fully automated workflow that: > Finds homes worth $600K-$2M > Detects large unused backyards with no pool > Generates a realistic before/after pool visualization > Estimates project cost and potential home value upside > Turns everything into a ready-to-send offer for the homeowner or contractor The valuable part is not that AI can make a pretty image. It’s that the buyer sees the outcome before anyone sells it to them. That changes the whole offer. You are not saying "we build pools." You are showing the exact pool that could exist in their backyard. And in the article below you’ll find 4 more AI automations that can turn boring workflows into real revenue

10,875 views • 9 days ago

THIS OPERATOR REPLACED A $50,000 AUTOMATION AGENCY WITH A 3-HOUR BUIL 378 browser tabs. 47 Notion pages nobody touched in 6 months. A spreadsheet that crashes every Monday He didn't hire a dev team. He didn't buy n8n enterprise He cloned a GitHub repo, wired 3 API keys, and typed /onboard into Claude Code By day 14 his team messaged the AI OS in Slack instead of messaging him His old workflow: 3 hours of context switching, 2 hours of actual work His new workflow: 5 hours of deep work, 30 minutes of admin This is not a dashboard. This is a command center Full breakdown below ↓

THIS OPERATOR REPLACED A $50,000 AUTOMATION AGENCY WITH A 3-HOUR BUIL 378 browser tabs. 47 Notion pages nobody touched in 6 months. A spreadsheet that crashes every Monday He didn't hire a dev team. He didn't buy n8n enterprise He cloned a GitHub repo, wired 3 API keys, and typed /onboard into Claude Code By day 14 his team messaged the AI OS in Slack instead of messaging him His old workflow: 3 hours of context switching, 2 hours of actual work His new workflow: 5 hours of deep work, 30 minutes of admin This is not a dashboard. This is a command center Full breakdown below ↓

10,158 views • 22 days ago

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