Founder & Product Engineer Building AI products that solve real problems. https://nitter.cf/t.co/YiU2s57jiV https://nitter.cf/t.co/3EDGC9YDAc https://nitter.cf/t.co/e8rQiHKiBB (MenuOS) tem slowsip
All I do now 👉
Joined November 2024
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Someone built a free AI engineering curriculum on GitHub.
523 lessons.
20 phases.
~342 hours.
You start with the math, then build backprop, tokenizers, attention, LLMs and agents from scratch.
60K+ stars.
This is seriously worth bookmarking.
github.com/rohitg00/ai-engin…
🔶 HOW YOU GO VIRAL
Every action a user takes on your posts either boosts it, or docks it
Likes are good, but replies and sharing links are SO MUCH BETTER
In fact, clicking share link is worth 40x a like
Stop optimizing for likes. Start optimizing for shares
Shashank | Building AI Products retweeted
How can you not be optimistic?
How can you not wake up every day and not be motivated when men and women out there are making miracles possible .
These things used to happen in US, now they happen here. The motivation is right in your backyard.
How can you be a blackpiller when the future is in your hands?
Shashank | Building AI Products retweeted
My friend applied to 250 tech jobs in two years. No MIT. No Stanford.
Last month Anthropic offered him $750,000.
I asked him how he broke in from zero.
He sent me the exact video that got him in. Anthropic's 2-hour course on how to become an AI engineer in 2026.
Thariq Shihipar shows you exactly how to build AI agents from scratch.
I watched it last night.
Halfway through, I realized I could break into an AI lab in months, not years.
Bookmark this and read the article below.
• 00:00 - AI agent harness
• 23:44 - building AI agent loops
• 56:39 - AI agent context engineering
• 1:33:34 - AI agent deterministic hooks
• 1:50:31 - Anthropic SWE interview process
BAIDU JUST OPEN-SOURCED OCR MODEL THAT READS 40-PAGE DOCUMENTS IN ONE SHOT.
it's called Unlimited-OCR.
> processes the entire document instead of page by page
> preserves tables, formulas, and reading order
> runs locally on your machine
> outputs clean Markdown
no more paying
building trained.chat in public.
shipped the product flow:
knowledge → agent → deploy → conversations → evaluation → improvement.
also narrowed the initial market and built the first investor deck.
still not MVP ready.
just building one system at a time.
internships used to be how students got experience.
now you can build a product, find users, make money, fail, and learn more in one summer than most internships teach in a year.
hard to blame them.
