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Engineering Map
Everything a senior engineer is expected to know
573 topics across the browser, React, backend, data, distributed systems and building with models. Every page says what a thing is, why it matters, and points at the best three to five things to read next — 1,415 links, hand-picked and re-checked on a schedule so they don't rot. No account, no ads, no upsell. Open source: MIT code, CC-licensed writing.
I kept losing the same explanations in bookmarks and half-remembered blog posts. There was no single place saying: here is the set of things a senior engineer is expected to have a real grasp of, and here is the best thing to read about each one.
So I wrote one. 573 topics across 8 sections — the browser, React, backend, data, system design, AI and LLM engineering, practices, and behavioural.
One rule decided what made it in: a topic earns a page if it can plausibly come up in a real interview round, or if you need it to answer something that does. Things that are real but never asked got cut. That is why there is no algorithms grind, and why the AI section is 70 topics — tool use, agents, MCP, retrieval, evals, cost, prompt injection. That part I could not find collected anywhere else.
Every page has the same shape: what it is in one line, what it is, why it matters, key points, then the links out. You learn where things are once and can skim any page after that.
The 1,415 outbound links took the longest. Three to five per topic, exactly one marked "start here", and a script re-checks every URL on a schedule — link rot is what actually kills a resource like this.
Free, no account, nothing to sign up for. MIT code, CC-licensed writing, all on GitHub.
I would love to know what is missing — especially from anyone who has run these loops from the other side of the table.
I will be around all day, so ask me anything.
About Engineering Map on Product Hunt
“Everything a senior engineer is expected to know”
Engineering Map was submitted on Product Hunt and earned 2 upvotes and 1 comments, placing #143 on the daily leaderboard. 573 topics across the browser, React, backend, data, distributed systems and building with models. Every page says what a thing is, why it matters, and points at the best three to five things to read next — 1,415 links, hand-picked and re-checked on a schedule so they don't rot. No account, no ads, no upsell. Open source: MIT code, CC-licensed writing.
On the analytics side, Engineering Map competes within Open Source, Education, Developer Tools and GitHub — topics that collectively have 708.3k followers on Product Hunt. The dashboard above tracks how Engineering Map performed against the three products that launched closest to it on the same day.
Who hunted Engineering Map?
Engineering Map was hunted by Prateek Rawat. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of Engineering Map including community comment highlights and product details, visit the product overview.
Prateek Rawat
Hey Product Hunt! 🚀
I am Prateek, and I built this for myself first.
I kept losing the same explanations in bookmarks and half-remembered blog posts. There was no single place saying: here is the set of things a senior engineer is expected to have a real grasp of, and here is the best thing to read about each one.
So I wrote one. 573 topics across 8 sections — the browser, React, backend, data, system design, AI and LLM engineering, practices, and behavioural.
One rule decided what made it in: a topic earns a page if it can plausibly come up in a real interview round, or if you need it to answer something that does. Things that are real but never asked got cut. That is why there is no algorithms grind, and why the AI section is 70 topics — tool use, agents, MCP, retrieval, evals, cost, prompt injection. That part I could not find collected anywhere else.
Every page has the same shape: what it is in one line, what it is, why it matters, key points, then the links out. You learn where things are once and can skim any page after that.
The 1,415 outbound links took the longest. Three to five per topic, exactly one marked "start here", and a script re-checks every URL on a schedule — link rot is what actually kills a resource like this.
Free, no account, nothing to sign up for. MIT code, CC-licensed writing, all on GitHub.
I would love to know what is missing — especially from anyone who has run these loops from the other side of the table.
I will be around all day, so ask me anything.