If the question is about inference, not business, start with max plans (Claude and Codex), ensure your orchestrator is model agnostic, create evals and harness to test different models and tune prompts/principles to hit quality thresholds, and if you can use local inference, start with runpod.
Once you're at ~$500-600/mo sustained (whether that's day 2 or year 2) look into hardware now you know the size of model you need and the token speed that'll be required to keep up with your workflow.
For small models, fast tokens, it's a gaming rig. For larger models some combination of Sparks (1 or 2) or Mac studios or even MacBook pros.
Most teams don’t have a data problem. They have an access problem.
We replaced SQL with a conversational interface and went from hours of waiting to 500 queries/day.
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You can now enable Claude to use your computer to complete tasks.
It opens your apps, navigates your browser, fills in spreadsheets—anything you'd do sitting at your desk.
Research preview in Claude Cowork and Claude Code, macOS only.
“Like to do” is becoming outdated. The new standard for getting things done will be people who love doing the work. That shift is already underway, and it’s hard to avoid.
ان بے شرموں کی قابلیت عام پاکستانی شہریوں کو گالیاں دینے تک تھی۔ڈنٹونک اور DGISPR نے ان ٹُوچوں سے جنگی کانٹینٹ بنوا دیا جس میں وہ پشتونوں کی توہین کر رہے ہیں۔ انکے مالکوں کو کوئی بتائے کہ سرحد کے دونوں طرف پشتون ہی ہیں۔انکے زمے گالیاں دینا ہی رہنے دیں۔
Most Go dataframe libs felt abandoned or clunky.
So I built my own.
🦦 Otters — a modern DataFrame library for Go
Fast. Idiomatic. Chainable.
https://t.co/74lycughve
#golang#buildinpublic