Show HN: Nova: Open-source solution for CAD file conflicts

Show HN (score: 5)

Found: October 19, 2025

ID: 1975

Description

Other

Show HN: Nova: Open-source solution for CAD file conflicts Hey HN,

A friend at a hardware startup mentioned how their engineering team struggles with CAD file conflicts as PDM solutions are not affordable. Multiple engineers opening the same SolidWorks part = corrupted files and lost work.

I was motivated and started building Nova. Nova is a open source file locking system, designed to support multiple CAD softwares with real time locking and live dashboard to keep design engineers in sync.

Nova is built with python and Next.js.

Get started with -

  git clone https://github.com/agg111/nova
  cd nova
  pip install -r requirements.txt
  nova start
  nova --help (for more commands)

Open http://localhost:3000 in browser

I am looking for early users to get some feedback and learn about more features or bottlenecks that mechanical design teams currently face.

More from Show

Show HN: WebGPU React Renderer Using Vello

Show HN: WebGPU React Renderer Using Vello I've built a package to use Raph Levien's Vello as a blazing fast 2D renderer for React on WebGPU. It uses WASM to hook into the Rust code

Show HN: On the edge of Apple Silicon memory speeds

Show HN: On the edge of Apple Silicon memory speeds I have developed open source CLI-tool for Apple Silicon macOS. It measures memory speeds in different ways and also latency. It can achieve up to 96-97% efficiency on read speed on M4 base what is advertised as 120GB/s. All memory operations are in assembly.I would really appreciate for results on different CPU's how benchmark works on those. I have been able to test this on M1 and M4.command : 'memory_benchmark -non-cacheable -count 5 -output results.JSON' (close all applications before running)This will generate JSON file where you find sections copy_gb_s, read_gb_s and write_gb_s statics.Example M4 with 10 loops: "copy_gb_s": { "statistics": { "average": 106.65421233311835, "max": 106.70240696071005, "median": 106.65069297260811, "min": 106.6336774994254, "p90": 106.66606919223108, "p95": 106.68423807647056, "p99": 106.69877318386216, "stddev": 0.01930653530818627 }, "values": [ 106.70240696071005, 106.66203166240008, 106.64410802226159, 106.65831409449595, 106.64148106986977, 106.6482935780762, 106.63974821679058, 106.65896986001393, 106.6336774994254, 106.65309236714002 ] }, "read_gb_s": { "statistics": { "average": 115.83111228356601, "max": 116.11098114619033, "median": 115.84480882265643, "min": 115.56959026587722, "p90": 115.99667266786554, "p95": 116.05382690702793, "p99": 116.09955029835784, "stddev": 0.1768243167963439 }, "values": [ 115.79154681380165, 115.56959026587722, 115.60574235736468, 115.72112860271632, 115.72147129262802, 115.89807083151123, 115.95527337086908, 115.95334642887214, 115.98397172582945, 116.11098114619033 ] }, "write_gb_s": { "statistics": { "average": 65.55966046805113, "max": 65.59040040480241, "median": 65.55933583741347, "min": 65.50911885624045, "p90": 65.5840272860955, "p95": 65.58721384544896, "p99": 65.58976309293172, "stddev": 0.02388146120866979 },Patterns benchmark also shows bit more of memory speeds. command: 'memory_benchmark -patterns -non-cacheable -count 5 -output patterns.JSON'Example M4 from 100 loops: "sequential_forward": { "bandwidth": { "read_gb_s": { "statistics": { "average": 116.38363691482549, "max": 116.61212708384109, "median": 116.41264548721367, "min": 115.449510036971, "p90": 116.54143114134801, "p95": 116.57314206456576, "p99": 116.60095068065866, "stddev": 0.17026641589059727 } } } }"strided_4096": { "bandwidth": { "read_gb_s": { "statistics": { "average": 26.460392735220456, "max": 27.7722419653915, "median": 26.457051473208285, "min": 25.519925729459107, "p90": 27.105171215736604, "p95": 27.190715938337473, "p99": 27.360449534513144, "stddev": 0.4730857335572576 } } } }"random": { "bandwidth": { "read_gb_s": { "statistics": { "average": 26.71367836895143, "max": 26.966820487564327, "median": 26.69907406197067, "min": 26.49374804466308, "p90": 26.845236287807374, "p95": 26.882004355057887, "p99": 26.95742242818151, "stddev": 0.09600564296001704 } } } }Thank you for reading :)

Show HN: Cachekit – High performance caching policies library in Rust

Show HN: AI video generator that outputs React instead of video files

Show HN: AI video generator that outputs React instead of video files Hey HN! This is Mayank from Outscal with a new update. Our website is now live. Quick context: we built a tool that generates animated videos from text scripts. The twist: instead of rendering pixels, it outputs React/TSX components that render as the video.Try it: <a href="https://ai.outscal.com/" rel="nofollow">https://ai.outscal.com/</a> Sample video: <a href="https://outscal.com/v2/video/ai-constraints-m7p3_v1/12-01-26-18-47-41" rel="nofollow">https://outscal.com/v2/video/ai-constraints-m7p3_v1/12-01-26...</a>You pick a style (pencil sketch or neon), enter a script (up to 2000 chars), and it runs: scene direction → ElevenLabs audio → SVG assets → Scene Design → React components → deployed video.What we learned building this:We built the first version on Claude Code. Even with a human triggering commands, agents kept going off-script — they had file tools and would wander off reading random files, exploring tangents, producing inconsistent output.The fix was counterintuitive: fewer tools, not more guardrails. We stripped each agent to only what it needed and pre-fed context instead of letting agents fetch it themselves.Quality improved immediately.We wouldn't launch the web version until this was solid. Moved to Claude Agent SDK, kept the same constraints, now fully automated.Happy to discuss the agent architecture, why React-as-video, or anything else.

No other tools from this source yet.