* Nia gives coding agents accurate context by indexing entire codebases, documentation, and packages. It fixes hallucinations by letting agents retrieve real source information instead of guessing. Developers ship faster because AI can read and understand their actual project. This is a different use case of Nia but apparently it also works.
You can chat with it and ask any question because it has access to Nia\u2019s knowledge base, which indexed all of his personal essays. The agent is able to call multiple tools that directly use Nia\u2019s API:
\u2022 NiaWebSearch - searches the web\n\u2022 searchEssays - semantic search over all essays\n\u2022 browseEssays - shows the full tree of essays\n\u2022 listDirectory - lists essays in a path\n\u2022 readEssay - reads full essay content\n\u2022 grepEssays - regex pattern search\n\u2022 getSourceContent - retrieves full source by identifier
Models:\nanthropic/claude-sonnet-4.5\nmoonshotai/kimi-k2-thinking\nxai/grok-4-fast-reasoning\nalibaba/qwen3-vl-thinking
It\u2019s fully free and open source.
Try it: https://www.paulgraham-nia.com/\nCode: https://github.com/nozomio-labs/paulgraham-ai"},"title":{"fullyHighlighted":false,"matchLevel":"partial","matchedWords":["ai"],"value":"Show HN: AI Paul Graham"},"url":{"matchLevel":"none","matchedWords":[],"value":"https://www.paulgraham-nia.com/"}},"_tags":["story","author_arlanrakh","story_46180304","show_hn"],"author":"arlanrakh","children":[46181142,46185347,46190254],"created_at":"2025-12-07T09:11:49Z","created_at_i":1765098709,"num_comments":5,"objectID":"46180304","points":4,"story_id":46180304,"story_text":"I built an AI version of Paul Graham, fully powered by the Nia API.
* Nia gives coding agents accurate context by indexing entire codebases, documentation, and packages. It fixes hallucinations by letting agents retrieve real source information instead of guessing. Developers ship faster because AI can read and understand their actual project. This is a different use case of Nia but apparently it also works.
You can chat with it and ask any question because it has access to Nia\u2019s knowledge base, which indexed all of his personal essays. The agent is able to call multiple tools that directly use Nia\u2019s API:
\u2022 NiaWebSearch - searches the web\n\u2022 searchEssays - semantic search over all essays\n\u2022 browseEssays - shows the full tree of essays\n\u2022 listDirectory - lists essays in a path\n\u2022 readEssay - reads full essay content\n\u2022 grepEssays - regex pattern search\n\u2022 getSourceContent - retrieves full source by identifier
Models:\nanthropic/claude-sonnet-4.5\nmoonshotai/kimi-k2-thinking\nxai/grok-4-fast-reasoning\nalibaba/qwen3-vl-thinking
It\u2019s fully free and open source.
Try it: https://www.paulgraham-nia.com/\nCode: https://github.com/nozomio-labs/paulgraham-ai","title":"Show HN: AI Paul Graham","updated_at":"2026-03-05T23:09:23Z","url":"https://www.paulgraham-nia.com/"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"marsninja"},"story_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["kimi","moonshot","ai"],"value":"I built this tool (kimi-fetch) to download the full 1.5TB moonshotai/Kimi-K3 from Hugging Face, one file at a time, and survive being stopped and restarted for any reason (Ctrl-C, kill -9, power loss).
If anything happens you can run it again and it resumes midfile until all 100 plus files are complete and verified with a sha256 hash.
The tool itself is a 68kb standalone binary that runs on mac and linux (wsl if you are on windows). And when you run it ./kimi-fetch it will immediately start downloading k3 from hungingface with max restart-ability. And will verify everything it downloads. I'll likely add a feature in a future release to have it automatically create 3 copies after for triple modular redundancy via a flag, but users can do that themselves for now.
I created this because I couldn't find a focused tool just for this type of thing, and I want to get this done with then next 24 hrs, because there is no telling what the government might do.
Its easy to install via a single curl command. And you simply have ot run ./kimi-fetch and it createa Kimi-K3 folder and gets going. Would love feedback."},"title":{"fullyHighlighted":false,"matchLevel":"partial","matchedWords":["kimi"],"value":"Show HN: Easiest tool (65kb binary) to download Kimi K3 fast (while we can)"},"url":{"fullyHighlighted":false,"matchLevel":"partial","matchedWords":["kimi"],"value":"https://github.com/marsninja/kimi-fetch"}},"_tags":["story","author_marsninja","story_49073376","show_hn"],"author":"marsninja","children":[49074333,49077309],"created_at":"2026-07-27T18:05:47Z","created_at_i":1785175547,"num_comments":2,"objectID":"49073376","points":4,"story_id":49073376,"story_text":"I built this tool (kimi-fetch) to download the full 1.5TB moonshotai/Kimi-K3 from Hugging Face, one file at a time, and survive being stopped and restarted for any reason (Ctrl-C, kill -9, power loss).
If anything happens you can run it again and it resumes midfile until all 100 plus files are complete and verified with a sha256 hash.
The tool itself is a 68kb standalone binary that runs on mac and linux (wsl if you are on windows). And when you run it ./kimi-fetch it will immediately start downloading k3 from hungingface with max restart-ability. And will verify everything it downloads. I'll likely add a feature in a future release to have it automatically create 3 copies after for triple modular redundancy via a flag, but users can do that themselves for now.
I created this because I couldn't find a focused tool just for this type of thing, and I want to get this done with then next 24 hrs, because there is no telling what the government might do.
Its easy to install via a single curl command. And you simply have ot run ./kimi-fetch and it createa Kimi-K3 folder and gets going. Would love feedback.","title":"Show HN: Easiest tool (65kb binary) to download Kimi K3 fast (while we can)","updated_at":"2026-07-29T02:08:40Z","url":"https://github.com/marsninja/kimi-fetch"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"kemyd"},"story_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["kimi","moonshot","ai"],"value":"Hey HN,
We built a different kind of AI benchmark for UI generation.
Instead of static leaderboards or curated screenshots, you can watch multiple models generate the same design live, side-by-side, and decide which output is actually better.
Under the hood, we call AI models from Anthropic (Opus), OpenAI (GPT), Google (Gemini), and Moonshot AI (Kimi).
Each model generates a real, editable project using Tailwind CSS (not screenshots or canvas exports). You can export it for Next.js, Laravel (Blade), Symfony (Twig), WordPress, or plain HTML.
What we noticed building this:
* Popular benchmarks don't reflect UX/UI quality. For a different prompt, one model is better than another (that's why live comparison on a single screen matters).
* Some models overuse wrappers/div soup. Some hallucinate layout constraints.
* Kimi likes Cyrillic, even if all other models won't use it for the same prompt.
The interesting part wasn't ranking models. It was making their outputs easier for humans to compare visually.
Short demo:\nhttps://www.youtube.com/watch?v=RCTZlvqMQdc
Curious whether this feels more useful than traditional leaderboard-style AI benchmarks.
Happy to answer technical questions.
Example for HN:
Prompt: Redesign the Hacker News website for 2030, including sample entries that could realistically appear on the platform in that year.
Results: https://shuffle.dev/ai-design/Tjjy7XAFMq25AI
Previews:
Opus: \nhttps://shuffle.dev/preview/d6d5ba4eeede381cee7e30c697f010c7...
GPT: \nhttps://shuffle.dev/preview/f050359977c1d6dc6c8fc104a24b83c3...
Gemini: \nhttps://shuffle.dev/preview/eab78f9748a6d8ccecb94a8b0390f044...
Kimi: \nhttps://shuffle.dev/preview/394bb596a8efa50342db4dc88c5f9fab..."},"title":{"fullyHighlighted":false,"matchLevel":"partial","matchedWords":["ai"],"value":"Show HN: Real-Time AI Design Benchmark"},"url":{"fullyHighlighted":false,"matchLevel":"partial","matchedWords":["ai"],"value":"https://shuffle.dev/ai-design"}},"_tags":["story","author_kemyd","story_47135831","show_hn"],"author":"kemyd","created_at":"2026-02-24T11:35:21Z","created_at_i":1771932921,"num_comments":0,"objectID":"47135831","points":2,"story_id":47135831,"story_text":"Hey HN,
We built a different kind of AI benchmark for UI generation.
Instead of static leaderboards or curated screenshots, you can watch multiple models generate the same design live, side-by-side, and decide which output is actually better.
Under the hood, we call AI models from Anthropic (Opus), OpenAI (GPT), Google (Gemini), and Moonshot AI (Kimi).
Each model generates a real, editable project using Tailwind CSS (not screenshots or canvas exports). You can export it for Next.js, Laravel (Blade), Symfony (Twig), WordPress, or plain HTML.
What we noticed building this:
* Popular benchmarks don't reflect UX/UI quality. For a different prompt, one model is better than another (that's why live comparison on a single screen matters).
* Some models overuse wrappers/div soup. Some hallucinate layout constraints.
* Kimi likes Cyrillic, even if all other models won't use it for the same prompt.
The interesting part wasn't ranking models. It was making their outputs easier for humans to compare visually.
Short demo:\nhttps://www.youtube.com/watch?v=RCTZlvqMQdc
Curious whether this feels more useful than traditional leaderboard-style AI benchmarks.
Happy to answer technical questions.
Example for HN:
Prompt: Redesign the Hacker News website for 2030, including sample entries that could realistically appear on the platform in that year.
Results: https://shuffle.dev/ai-design/Tjjy7XAFMq25AI
Previews:
Opus: \nhttps://shuffle.dev/preview/d6d5ba4eeede381cee7e30c697f010c7...
GPT: \nhttps://shuffle.dev/preview/f050359977c1d6dc6c8fc104a24b83c3...
Gemini: \nhttps://shuffle.dev/preview/eab78f9748a6d8ccecb94a8b0390f044...
Kimi: \nhttps://shuffle.dev/preview/394bb596a8efa50342db4dc88c5f9fab...","title":"Show HN: Real-Time AI Design Benchmark","updated_at":"2026-03-05T23:36:15Z","url":"https://shuffle.dev/ai-design"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"nateb2022"},"story_text":{"fullyHighlighted":false,"matchLevel":"partial","matchedWords":["kimi"],"value":"Related: Kimi-K3 Technical Report [pdf] - https://news.ycombinator.com/item?id=49070985"},"title":{"fullyHighlighted":false,"matchLevel":"partial","matchedWords":["kimi"],"value":"Kimi-K3 on HuggingFace"},"url":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["kimi","moonshot","ai"],"value":"https://huggingface.co/moonshotai/Kimi-K3"}},"_tags":["story","author_nateb2022","story_49065752"],"author":"nateb2022","children":[49065757,49065868,49066164,49066165,49066200,49066201,49066235,49066240,49066343,49066351,49066354,49066398,49066399,49066492,49066495,49066509,49066536,49066552,49066672,49066733,49066752,49066917,49066981,49066990,49067146,49067204,49067206,49067228,49067369,49067492,49067498,49067501,49067514,49067628,49067657,49067699,49067738,49068616,49068905,49068959,49069140,49069214,49069344,49069392,49069841,49070385,49070397,49070490,49070499,49070527,49070707,49070732,49070758,49070833,49070851,49070887,49070898,49070962,49070979,49070982,49071072,49071410,49072873,49073251,49073254,49073269,49073424,49073571,49073615,49073821,49075078,49075205,49075315,49076001,49076242,49076281,49078395,49078596,49080388,49120431],"created_at":"2026-07-27T06:18:10Z","created_at_i":1785133090,"num_comments":544,"objectID":"49065752","points":1381,"story_id":49065752,"story_text":"Related: Kimi-K3 Technical Report [pdf] - https://news.ycombinator.com/item?id=49070985","title":"Kimi-K3 on HuggingFace","updated_at":"2026-08-08T00:15:28Z","url":"https://huggingface.co/moonshotai/Kimi-K3"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"nekofneko"},"title":{"fullyHighlighted":false,"matchLevel":"partial","matchedWords":["kimi"],"value":"Kimi K2.7-Code: open-source coding model with better token efficiency"},"url":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["kimi","moonshot","ai"],"value":"https://huggingface.co/moonshotai/Kimi-K2.7-Code"}},"_tags":["story","author_nekofneko","story_48502347"],"author":"nekofneko","children":[48502638,48502661,48502859,48503148,48503161,48503169,48503278,48503345,48503463,48503528,48503581,48503595,48503625,48503638,48503855,48504011,48504348,48504565,48505405,48505456,48505949,48506499,48507096,48507538,48507923,48514054,48537804,48538566],"created_at":"2026-06-12T10:42:50Z","created_at_i":1781260970,"num_comments":240,"objectID":"48502347","points":463,"story_id":48502347,"title":"Kimi K2.7-Code: open-source coding model with better token efficiency","updated_at":"2026-08-08T09:22:44Z","url":"https://huggingface.co/moonshotai/Kimi-K2.7-Code"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"vinhnx"},"story_text":{"fullyHighlighted":false,"matchLevel":"partial","matchedWords":["kimi"],"value":"Related: Kimi-K3 on HuggingFace - https://news.ycombinator.com/item?id=49065752"},"title":{"fullyHighlighted":false,"matchLevel":"partial","matchedWords":["kimi"],"value":"Kimi-K3 Technical Report [pdf]"},"url":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["kimi","moonshot","ai"],"value":"https://github.com/MoonshotAI/Kimi-K3/blob/main/k3_tech_report.pdf"}},"_tags":["story","author_vinhnx","story_49070985"],"author":"vinhnx","children":[49071129,49071185,49071249,49071277,49071313,49071435,49071483,49071507,49071516,49071529,49071548,49071555,49071564,49071662,49071908,49072022,49072105,49072109,49072173,49072745,49074458,49074612,49074700,49074819,49075045,49082130],"created_at":"2026-07-27T15:23:45Z","created_at_i":1785165825,"num_comments":183,"objectID":"49070985","points":391,"story_id":49070985,"story_text":"Related: Kimi-K3 on HuggingFace - https://news.ycombinator.com/item?id=49065752","title":"Kimi-K3 Technical Report [pdf]","updated_at":"2026-08-05T05:15:04Z","url":"https://github.com/MoonshotAI/Kimi-K3/blob/main/k3_tech_report.pdf"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"vinhnx"},"title":{"fullyHighlighted":false,"matchLevel":"partial","matchedWords":["kimi"],"value":"Kimi K2.5 Technical Report [pdf]"},"url":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["kimi","moonshot","ai"],"value":"https://github.com/MoonshotAI/Kimi-K2.5/blob/master/tech_report.pdf"}},"_tags":["story","author_vinhnx","story_46826597"],"author":"vinhnx","children":[46828650,46828776,46828903,46829045,46829170,46829272,46829352,46829977,46830275,46831013,46831104,46831371,46831419,46831485,46831757,46831790,46832278,46833409,46833693,46834096,46834169,46834336,46835609,46836341,46836819,46837404,46838184,46838830],"created_at":"2026-01-30T16:43:50Z","created_at_i":1769791430,"num_comments":141,"objectID":"46826597","points":388,"story_id":46826597,"title":"Kimi K2.5 Technical Report [pdf]","updated_at":"2026-06-11T09:47:42Z","url":"https://github.com/MoonshotAI/Kimi-K2.5/blob/master/tech_report.pdf"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"ConteMascetti71"},"title":{"fullyHighlighted":false,"matchLevel":"partial","matchedWords":["kimi"],"value":"Kimi K2 is a state-of-the-art mixture-of-experts (MoE) language model"},"url":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["kimi","moonshot","ai"],"value":"https://github.com/MoonshotAI/Kimi-K2"}},"_tags":["story","author_ConteMascetti71","story_44543508"],"author":"ConteMascetti71","children":[44544036,44544300,44548861],"created_at":"2025-07-12T17:26:24Z","created_at_i":1752341184,"num_comments":2,"objectID":"44543508","points":352,"story_id":44543508,"title":"Kimi K2 is a state-of-the-art mixture-of-experts (MoE) language model","updated_at":"2025-10-27T09:17:14Z","url":"https://github.com/MoonshotAI/Kimi-K2"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"blackcat201"},"title":{"fullyHighlighted":false,"matchLevel":"partial","matchedWords":["kimi"],"value":"Kimi Linear: An Expressive, Efficient Attention Architecture"},"url":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["kimi","moonshot","ai"],"value":"https://github.com/MoonshotAI/Kimi-Linear"}},"_tags":["story","author_blackcat201","story_45766937"],"author":"blackcat201","children":[45768006,45768363,45768708,45769528,45769733,45769758,45771056,45772041,45772203,45772588,45774379],"created_at":"2025-10-31T00:07:36Z","created_at_i":1761869256,"num_comments":47,"objectID":"45766937","points":217,"story_id":45766937,"title":"Kimi Linear: An Expressive, Efficient Attention Architecture","updated_at":"2026-07-28T19:20:53Z","url":"https://github.com/MoonshotAI/Kimi-Linear"}],"hitsPerPage":20,"nbHits":66,"nbPages":4,"page":0,"params":"query=kimi+moonshot+ai&tags=story&advancedSyntax=true&analyticsTags=backend","processingTimeMS":13,"processingTimingsMS":{"_request":{"roundTrip":25},"fetch":{"query":9,"scanning":2,"total":12},"total":14},"query":"kimi moonshot ai","serverTimeMS":15}