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Atlas
Deterministic code intelligence for developers and AI
Atlas helps developers and AI coding assistants understand any codebase with confidence. Find symbols, trace callers and dependencies, assess change impact, and retrieve focused context with exact file-and-line citations. Results are deterministic and reproducible. Atlas runs locally as a single binary with SQLite, CLI, MCP, and HTTP; your code never leaves your machine. Published benchmarks show 36× fewer context tokens and 17× faster retrieval than the tested graph baseline.
We built Atlas after seeing AI coding tools consume huge prompts yet still miss the few files and relationships that actually matter. Our goal was a local map of the codebase: compact, source-grounded context for humans and assistants without sending repositories to a hosted service. Atlas indexes symbols, references, callers, routes, and likely change impact into SQLite, then returns bounded answers with file:line citations through CLI, MCP, or HTTP. We’d love feedback on language coverage, workflows, and the queries you want Atlas to answer next.
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About Atlas on Product Hunt
“Deterministic code intelligence for developers and AI”
Atlas was submitted on Product Hunt and earned 4 upvotes and 1 comments, placing #159 on the daily leaderboard. Atlas helps developers and AI coding assistants understand any codebase with confidence. Find symbols, trace callers and dependencies, assess change impact, and retrieve focused context with exact file-and-line citations. Results are deterministic and reproducible. Atlas runs locally as a single binary with SQLite, CLI, MCP, and HTTP; your code never leaves your machine. Published benchmarks show 36× fewer context tokens and 17× faster retrieval than the tested graph baseline.
Atlas was featured in Developer Tools (516.3k followers), Artificial Intelligence (474.3k followers) and GitHub (41.3k followers) on Product Hunt. Together, these topics include over 211.4k products, making this a competitive space to launch in.
Who hunted Atlas?
Atlas was hunted by Anand Ramachanadran. 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.
Want to see how Atlas stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Anand Ramachanadran