Hacker News

Article URL: https://www.pbs.org/show/channel-3-moscow/

Comments URL: https://news.ycombinator.com/item?id=43906945

Points: 3

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Article URL: https://www.betterbird.eu/

Comments URL: https://news.ycombinator.com/item?id=43906922

Points: 2

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Article URL: https://saewitz.com/code-lifecycles

Comments URL: https://news.ycombinator.com/item?id=43906524

Points: 1

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Article URL: https://web.archive.org/web/20201215120226/http://prelectur.stanford.edu/lecturers/hofstadter/analogy.html

Comments URL: https://news.ycombinator.com/item?id=43906520

Points: 1

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Article URL: https://www.zdnet.com/article/ibm-introduces-a-mainframe-for-ai-the-linuxone-emperor-5/

Comments URL: https://news.ycombinator.com/item?id=43906517

Points: 1

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Article URL: https://medium.com/@pmarreck/the-most-annoying-ui-problem-r3ditm-31773fe6bbd5

Comments URL: https://news.ycombinator.com/item?id=43906515

Points: 1

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Article URL: https://jerf.org/iri/post/2025/on_layers_and_boxes_and_lines/

Comments URL: https://news.ycombinator.com/item?id=43906510

Points: 1

# Comments: 0

Article URL: https://grafana.com/docs/grafana/latest/whatsnew/whats-new-in-v12-0/

Comments URL: https://news.ycombinator.com/item?id=43906506

Points: 1

# Comments: 0

We’ve built a micro-agency that delivers high-quality, ready-to-train AI datasets (image, text, tabular) in 24–72 hours.

Teams come to us when: - They need custom domain data fast (e.g. chatbot Q&A, niche classification, visual annotation sets) - They don’t want to deal with scraping, cleaning, or labeling - They’re stuck waiting for internal data teams

Examples we've delivered recently: - 850 pose photos annotated COCO-style (for Stable Diffusion LoRA) - 3,000 customer support Q&A pairs (OpenAI fine-tuning) - 7,800 fandom wiki entries converted into LLaMA lore Q&A bot - Japanese sarcasm classifier dataset - Contract clause classifier (PDF > JSONL)

We handle the full pipeline: scraping → labeling → formatting → delivery. Happy to chat use case ideas or share behind-the-scenes if you're building something similar.

https://dataset24.notion.site Or ping me here — first few projects are discounted while we iterate.

Comments URL: https://news.ycombinator.com/item?id=43906487

Points: 1

# Comments: 0

I've built MCPBar (https://www.mcp.bar), an open-source CLI tool that brings npm-like package management to the Model Context Protocol (MCP) ecosystem, solving the fragmentation problem for AI tools.

What is MCPBar?

- A package manager for discovering and installing MCP servers (which connect AI models to tools/APIs)

- Uses a standardized mcp.json manifest format (similar to package.json) (link: https://github.com/in-fun/mcpbar/blob/main/doc/proposal.md)

- Works across multiple MCP clients like Claude, Cursor, and Windsurf

- Includes an open registry with standardized metadata for MCP servers

Why I built it:

The MCP ecosystem is growing rapidly but lacks standardization. Each AI client implements MCP differently, making discovery and installation frustratingly manual. MCPBar creates a decentralized-yet-standardized approach to package management for AI tools.

Technical highlights:

- Decentralized registry pattern inspired by npm and ESM imports

- Manifest files contain standardized metadata and installation instructions

- Cross-platform support (works on macOS/Windows/Linux)

- Simple CLI interface: mcpbar install github/github-mcp-server

Try it:

npm install -g mcpbar

mcpbar search github # Find GitHub-related MCP servers

mcpbar install github/github-mcp-server # Install a GitHub MCP server

GitHub: https://github.com/in-fun/mcpbar

Project site: https://www.mcp.bar

I'd love feedback on the manifest format, the CLI interface, and whether this approach makes sense for the evolving MCP ecosystem. Are there other features you'd want this to support?

Comments URL: https://news.ycombinator.com/item?id=43906473

Points: 1

# Comments: 0

Article URL: https://clarity.microsoft.com/blog/why-microsoft-clarity-chose-clickhouse/

Comments URL: https://news.ycombinator.com/item?id=43906465

Points: 1

# Comments: 0

Hi everyone, my name is Kevin Lin, and this is a Show HN for my sheet music smart glasses project. My video was on the front page on Friday: https://news.ycombinator.com/item?id=43876243, but dang said we should do a Show HN as well, so here goes!

I’ve wanted to put sheet music into smart glasses for a long time, but the perfect opportunity to execute came in mid-February, when Mentra (YC W25) tweeted about a smart glasses hackathon they were hosting - winners would get to take home a pair. I went, had a blast making a bunch of music-related apps with my teammate, and we won, so I got to take them home, refine the project, and make a pretty cool video about it (https://www.youtube.com/watch?v=j36u2i7PKKE).

The glasses are Even Realities G1s. They look normal, but they have two microphones, a screen in each lens, and can be even made with a prescription. Every person I’ve met who tried them on was surprised at how good the display is, and the video recordings of them unfortunately don’t do them justice.

The software runs on AugmentOS, which is Mentra’s smart glasses operating system that works on various 3rd-party smart glasses, including the G1s. All I had to do to make an app was write and run a typescript file using the AugmentOS SDK. This gives you the voice transcription and raw audio as input, and text or bitmaps available as output to the screens, everything else is completely abstracted away. Your glasses communicate with an AugmentOS app, and then the app communicates with your typescript service.

The only hard part was creating a Python script to turn sheet music (MusicXML format) into small, optimized bitmaps to display on the screens. To start, the existing landscape of music-related Python libraries is pretty poorly documented and I ran into multiple never-before-seen error messages. Downscaling to the small size of the glasses screens also meant that stems and staff lines were disappearing, so I thought to use morphological dilation to emphasize those without making the notes unintelligible. The final pipeline was MusicXML -> music21 library to render chunks of bars to png -> dilate with opencv- > downscale -> convert to bitmap with Pillow -> optimize bitmaps with imagemagick. This is far from the best code I’ve ever written, but the LLMs attempt at this whole task was abysmal and my years of Python experience really got to shine here. The code is on GitHub: https://github.com/kevinlinxc/AugmentedChords.

Putting it together, my typescript service serves these bitmaps locally when requested. I put together a UI where I can navigate menus and sheet music with voice commands (e.g. show catalog, next, select, start, exit, pause) and then I connected foot pedals to my laptop. Because of bitmap sending latency (~3s right now, but future glasses will do better), using foot pedals to turn the bars while playing wasn’t viable, so I instead had one of my pedals toggle autoscrolling, and the other two pedals sped up/temporarily paused the scrolling.

After lots of adjustments, I was able to play a full song using just the glasses! It took many takes and there was definitely lots of room for improvement. For example: - Bitmap sending is pretty slow, which is why using the foot pedals to turn bars wasn’t viable; - The resolution is pretty small, I would love to put more bars in at once so I can flip less frequently; - Since foot pedals aren’t portable, it would be cool to have a mode where the audio dictates when the sheet music changes. I tried implementing that with FFT but it was often wrong and more effort is needed. Head tilt controls would be cool too, because full manual control is a hard requirement for practicing.

All of these pain points are being targeted by Mentra and other companies competing in the space, and so I’m super excited to see the next generation! Also, feel free to ask me anything!

Comments URL: https://news.ycombinator.com/item?id=43906442

Points: 6

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Article URL: https://www.wired.com/story/cbp-photo-everyone-in-vehicle-us-border/

Comments URL: https://news.ycombinator.com/item?id=43906436

Points: 1

# Comments: 1

Article URL: https://www.rockstargames.com/newswire/article/3928aaa9471o3a/grand-theft-auto-vi-watch-trailer-2-now

Comments URL: https://news.ycombinator.com/item?id=43906393

Points: 2

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Article URL: https://arxiv.org/abs/2504.14064

Comments URL: https://news.ycombinator.com/item?id=43906379

Points: 1

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Article URL: https://www.youtube.com/watch?v=IBzY06tH0sQ

Comments URL: https://news.ycombinator.com/item?id=43906365

Points: 1

# Comments: 0

Article URL: https://www.cloudquery.io/blog/how-we-handle-billion-row-clickhouse-inserts-with-uuid-range-bucketing

Comments URL: https://news.ycombinator.com/item?id=43906357

Points: 2

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Article URL: https://github.com/Milkdown/milkdown

Comments URL: https://news.ycombinator.com/item?id=43906355

Points: 1

# Comments: 0

Hey HN! We’re Vaibhav and Marcello. We’re building Plexe (https://github.com/plexe-ai/plexe), an open-source agent that turns natural language task descriptions into trained ML models. Here’s a video walkthrough: https://www.youtube.com/watch?v=bUwCSglhcXY.

There are all kinds of uses for ML models that never get realized because the process of making them is messy and convoluted. You can spend months trying to find the data, clean it, experiment with models and deploy to production, only to find out that your project has been binned for taking so long. There are many tools for “automating” ML, but it still takes teams of ML experts to actually productionize something of value. And we can’t keep throwing LLMs at every ML problem. Why use a generic 10B parameter language model, if a logistic regression trained on your data could do the job better?

Our light-bulb moment was that we could use LLMs to generate task-specific ML models that would be trained on one’s own data. Thanks to the emergent reasoning ability of LLMs, it is now possible to create an agentic system that might automate most of the ML lifecycle.

A couple of months ago, we started developing a Python library that would let you define ML models on structured data using a description of the expected behaviour. Our initial implementation arranged potential solutions into a graph, using LLMs to write plans, implement them as code, and run the resulting training script. Using simple search algorithms, the system traversed the solution space to identify and package the best model.

However, we ran into several limitations, as the algorithm proved brittle under edge cases, and we kept having to put patches for every minor issue in the training process. We decided to rethink the approach, throw everything out, and rebuild the tool using an agentic approach prioritising generality and flexibility. What started as a single ML engineering agent turned into an agentic ML "team", with all experiments tracked and logged using MLFlow.

Our current implementation uses the smolagents library to define an agent hierarchy. We mapped the functionality of our previous implementation to a set of specialized agents, such as an “ML scientist” that proposes solution plans, and so on. Each agent has specialized tools, instructions, and prompt templates. To facilitate cross-agent communication, we implemented a shared memory that enables objects (datasets, code snippets, etc) to be passed across agents indirectly by referencing keys in a registry. You can find a detailed write-up on how it works here: https://github.com/plexe-ai/plexe/blob/main/docs/architectur...

Plexe’s early release is focused on predictive problems over structured data, and can be used to build models such as forecasting player injury risk in high-intensity sports, product recommendations for an e-commerce marketplace, or predicting technical indicators for algorithmic trading. Here are some examples to get you started: https://github.com/plexe-ai/plexe/tree/main/examples

To get it working on your data, you can dump any CSV, parquet, etc and Plexe uses what it needs from your dataset to figure out what features it should use. In the open-source tool, it only supports adding files right now but in our platform version, we'll have support for integrating with Postgres where it pulls all available data based on an SQL query and dumps it into a parquet file for the agent to build models.

Next up, we’ll be tackling more of the ML project lifecycle: we’re currently working on adding a “feature engineering agent” that focuses on the complex data transformations that are often required for data to be ready for model training. If you're interested, check Plexe out and let us know your thoughts!

Comments URL: https://news.ycombinator.com/item?id=43906346

Points: 7

# Comments: 1

Free to download, fully SVG files. You can use as a logo or placeholders, no attribution required.

Comments URL: https://news.ycombinator.com/item?id=43906345

Points: 1

# Comments: 1