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Article URL: https://arxiv.org/abs/2503.07782

Comments URL: https://news.ycombinator.com/item?id=43477620

Points: 1

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Article URL: https://www.vanityfair.com/news/story/nick-denton-interview-thiel-musk

Comments URL: https://news.ycombinator.com/item?id=43477607

Points: 2

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Article URL: https://github.com/Capsize-Games/airunner/wiki/Text%E2%80%90to%E2%80%90Speech

Comments URL: https://news.ycombinator.com/item?id=43477598

Points: 1

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Article URL: https://github.com/sashahilton00/json2protobuf

Comments URL: https://news.ycombinator.com/item?id=43477596

Points: 1

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Article URL: https://briefs.video/videos/why-is-everything-binary/

Comments URL: https://news.ycombinator.com/item?id=43477582

Points: 1

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Article URL: https://www.t3.com/tech/phones/eu-paves-the-way-for-iphones-and-android-devices-to-ditch-usb-c-entirely

Comments URL: https://news.ycombinator.com/item?id=43477580

Points: 1

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Article URL: https://www.cbsnews.com/news/23andme-data-bankrupt-privacy/

Comments URL: https://news.ycombinator.com/item?id=43477577

Points: 2

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Article URL: https://www.ft.com/content/29ae4126-7263-47e7-bd2e-c79ad41d930d

Comments URL: https://news.ycombinator.com/item?id=43477574

Points: 2

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Article URL: https://www.cnn.com/2025/03/25/science/two-fingered-claw-dinosaur-duonychus-tsogtbaatari-mongolia/index.html

Comments URL: https://news.ycombinator.com/item?id=43477549

Points: 2

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Article URL: https://en.wikipedia.org/wiki/Stars_and_bars_(combinatorics)

Comments URL: https://news.ycombinator.com/item?id=43477515

Points: 1

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Article URL: https://developer.cisco.com/site/sandbox/

Comments URL: https://news.ycombinator.com/item?id=43477490

Points: 1

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Article URL: https://enterthenostr.com/

Comments URL: https://news.ycombinator.com/item?id=43477453

Points: 3

# Comments: 1

Article URL: https://www.engadget.com/entertainment/music/napster-just-sold-for-207-million-154432315.html

Comments URL: https://news.ycombinator.com/item?id=43477398

Points: 2

# Comments: 0

Article URL: https://en.wikipedia.org/wiki/Franz_Edelman_Award_for_Achievement_in_Operations_Research_and_the_Management_Sciences

Comments URL: https://news.ycombinator.com/item?id=43477359

Points: 1

# Comments: 0

Article URL: https://milgrom.people.stanford.edu/news/informs-2018-franz-edelman-award/

Comments URL: https://news.ycombinator.com/item?id=43477345

Points: 1

# Comments: 0

Article URL: https://www.dropbox.com/scl/fi/5abdbkwtnjxd635vcteys/796b9ef7c4e868d58a3e5d306cc8f425.pdf?dl=0&noscript=1&rlkey=7nwrto4wi3svfs7a3dui10gdd&st=khyfefag

Comments URL: https://news.ycombinator.com/item?id=43477336

Points: 1

# Comments: 0

tl;dr: I built a Reddit MCP server to make LLM-based research tasks faster and more accurate — especially for use cases where up-to-date or subreddit-specific info matters.

I’ve been bouncing between LLMs for "deep research" tasks lately — trend discovery, digging into niche use cases, finding the right community for topic . Reddit usually has the best signal for these, but getting reliable results from an LLM is tricky.

A few recurring issues: - Hallucinated subreddits: even with simple questions like “What’s the best subreddit for ?”, models often return made-up names. - Knowledge cutoffs: LLMs miss recent or trending content and topics, even when it’s easy to find on Reddit (try asking Claude what MCP is…) - Slow web tools: ChatGPT’s “Deep Research” and similar products can work to get up to date info, but they’re slow — often taking 5+ minutes to answer anything but the most trivial queries.

To make this faster and more consistent, I built a plug-and-play MCP server that connects directly to the Reddit API. It exposes a small set of structured tools (search, fetch, browse) that LLMs can use to retrieve Reddit posts and comments in real time. It’s read-only for now to prevent any unintended side effects, but I might add write access in the future.

Here’s a demo showing a request that a regular LLM without any tools simply could not complete at all, and a tool like ChatGPT Deep Research will take >5 mins and return worse results: asking the model to find highly upvoted comments in niche LLM subreddits. Demo video (sped up ~3x to reduce file size): https://github.com/user-attachments/assets/a2e9f2dd-a9ac-453...

The model runs a toolchain with a set of structured queries first to identify “niche” subreddits, then looks for promising posts and comments, and iterates on this until it thinks it’s satisfied the original request. It then returns a grounded answer — all in <2.5 minutes.

You can run this locally with Claude Desktop or any other MCP client (I actually use it with AutoGen -- example in the repo) — just plug in your Reddit API keys and go. There’s more in the README if you want to try it out, and I’m happy to provide any help as the MCP ecosystem is… not mature yet. Despite being a professional software engineer, building an MCP server was a total PITA so happy to pay it forward and help anyone in need of some guidance for anything they’re building.

Would love feedback — especially if you’ve been working on research tools, using MCP in novel ways, have a use case for this, or things you want to see in this space.

Comments URL: https://news.ycombinator.com/item?id=43477313

Points: 1

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Article URL: https://arxiv.org/abs/1910.11944

Comments URL: https://news.ycombinator.com/item?id=43477290

Points: 1

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Article URL: https://safeguar.de/

Comments URL: https://news.ycombinator.com/item?id=43477282

Points: 1

# Comments: 0

Article URL: https://billbennett.co.nz/ipad-pro-writing-field-guide/

Comments URL: https://news.ycombinator.com/item?id=43477272

Points: 4

# Comments: 0