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Hey Product Hunt! Meet HAI 1.0: a 1.2GB AI model that outperforms GPT-5 on coding benchmarks while running entirely offline on your laptop. Built with a novel neuro-symbolic architecture, it combines neural pattern recognition with symbolic logic for exceptional software engineering performance. On SWE-bench Lite, HAI 1.0 scored 68.4%, beating much larger models. Open-source, local, private, and subscription-free—try it today! email: [email protected]
Curious how it actually holds up on larger codebases beyond benchmark tasks. Does the neuro-symbolic architecture slow down noticeably when working through multi-file refactors, or does the performance stay consistent on real-world projects?
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About HAI 1.0 The 1.2GB AI that outcodes GPT5 on Product Hunt
“HAI 1.0: 1.2GB local AI. Beats GPT-5 at coding”
HAI 1.0 The 1.2GB AI that outcodes GPT5 was submitted on Product Hunt and earned 0 upvotes and 1 comments, placing #51 on the daily leaderboard. Hey Product Hunt! Meet HAI 1.0: a 1.2GB AI model that outperforms GPT-5 on coding benchmarks while running entirely offline on your laptop. Built with a novel neuro-symbolic architecture, it combines neural pattern recognition with symbolic logic for exceptional software engineering performance. On SWE-bench Lite, HAI 1.0 scored 68.4%, beating much larger models. Open-source, local, private, and subscription-free—try it today! email: [email protected]
HAI 1.0 The 1.2GB AI that outcodes GPT5 was featured in Developer Tools (515.5k followers), Artificial Intelligence (473.1k followers), GitHub (41.3k followers) and Tech (627.5k followers) on Product Hunt. Together, these topics include over 370.2k products, making this a competitive space to launch in.
Who hunted HAI 1.0 The 1.2GB AI that outcodes GPT5?
HAI 1.0 The 1.2GB AI that outcodes GPT5 was hunted by hillel ilany freedman. 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.
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Curious how it actually holds up on larger codebases beyond benchmark tasks. Does the neuro-symbolic architecture slow down noticeably when working through multi-file refactors, or does the performance stay consistent on real-world projects?