The best Hacker News stories from Show from the past day
Latest posts:
Show HN: The HTML Maze – Escape an eerie labyrinth built with HTML pages
Show HN: PlutoFilter- A single-header, zero-allocation image filter library in C
Show HN: PlutoFilter- A single-header, zero-allocation image filter library in C
Show HN: PlutoFilter- A single-header, zero-allocation image filter library in C
Show HN: PlutoFilter- A single-header, zero-allocation image filter library in C
Show HN: DataRamen, a Fast SQL Explorer with Automatic Joins and Data Navigation
I built DataRamen, a local-first SQL explorer that helps you get the data you need fast, without writing repetitive queries every time.<p>You run it locally from the CLI (no cloud version yet), connect your databases, and you're ready to go. The goal is to let you explore and query data like you would in a spreadsheet: intuitive, fast, and without friction.<p>Key features:<p>- Automatic joins & related data navigation: Right-click any row to instantly see related records in other tables (based on foreign keys or references).<p>- Keyboard-driven UI: Hit N to jump to a table, F to filter, and so on, it’s optimized for speed so you can go from question to insight in seconds (this point is still in progress, I find it confortable, but the goal is to make it even better).<p>- Named tabs with saved queries: Keep multiple tabs open with different queries, useful for comparing or cross-checking data. Tabs are saved, so you can get back to your queries at any time.<p>- Instant edit & insert: One click to edit or add rows, no need to write full queries.<p>- Multi-DB support: Connect several databases and search across all of them.<p>- Search across all columns: Find what you need even if you don't know the exact column.<p>If you've ever felt slowed down by writing the same SQL over and over just to explore your data, this might save you a ton of time. I’d love feedback or suggestions, especially from folks who wrangle data often.<p>Find more information on <a href="https://dataramen.xyz" rel="nofollow">https://dataramen.xyz</a><p>PS. don't be harsh on the logo, I did my best :)
Show HN: An MCP server that gives LLMs temporal awareness and time calculation
This is an open‑source Model Context Protocol (MCP) server that gives any LLM a sense of the passage of time.<p>Most MCP demos wire LLMs to external data stores. That’s useful, but MCP is also a chance to give models <i>perception</i> — extra senses beyond the prompt text.<p>Six functions (`current_datetime`, `time_difference`, `timestamp_context`, etc.) give Claude/GPT real temporal awareness: It can spot pauses, reason about rhythms, and even label a chat’s “three‑act structure”. Runs locally in <60 s (Python) or via a hosted demo.<p>If time works, what else could we surface? - Location / movement (GPS, speed, “I’m on a train”) - Weather (rainy evening vs clear morning) - Device state (battery low, poor bandwidth) - Ambient modality (user is dictating on mobile vs typing at desk) - Calendar context (meeting starts in 5 min) - Biometric cues (heart‑rate spikes while coding)<p>Curious what other signals people think would unlock better collaboration.<p>Full back story: <a href="https://medium.com/@jeremie.lumbroso/teaching-ai-the-significance-of-the-passage-of-time-yes-that-one-106ad7d20957" rel="nofollow">https://medium.com/@jeremie.lumbroso/teaching-ai-the-signifi...</a><p>Happy to discuss MCP patterns, tool discovery, or future “senses”. Feedback and PRs welcome!
Show HN: An MCP server that gives LLMs temporal awareness and time calculation
This is an open‑source Model Context Protocol (MCP) server that gives any LLM a sense of the passage of time.<p>Most MCP demos wire LLMs to external data stores. That’s useful, but MCP is also a chance to give models <i>perception</i> — extra senses beyond the prompt text.<p>Six functions (`current_datetime`, `time_difference`, `timestamp_context`, etc.) give Claude/GPT real temporal awareness: It can spot pauses, reason about rhythms, and even label a chat’s “three‑act structure”. Runs locally in <60 s (Python) or via a hosted demo.<p>If time works, what else could we surface? - Location / movement (GPS, speed, “I’m on a train”) - Weather (rainy evening vs clear morning) - Device state (battery low, poor bandwidth) - Ambient modality (user is dictating on mobile vs typing at desk) - Calendar context (meeting starts in 5 min) - Biometric cues (heart‑rate spikes while coding)<p>Curious what other signals people think would unlock better collaboration.<p>Full back story: <a href="https://medium.com/@jeremie.lumbroso/teaching-ai-the-significance-of-the-passage-of-time-yes-that-one-106ad7d20957" rel="nofollow">https://medium.com/@jeremie.lumbroso/teaching-ai-the-signifi...</a><p>Happy to discuss MCP patterns, tool discovery, or future “senses”. Feedback and PRs welcome!
Show HN: 0xDEAD//TYPE – A fast-paced typing shooter with retro vibes
Show HN: 0xDEAD//TYPE – A fast-paced typing shooter with retro vibes
Show HN: 0xDEAD//TYPE – A fast-paced typing shooter with retro vibes
Show HN: 0xDEAD//TYPE – A fast-paced typing shooter with retro vibes
Show HN: A 'Choose Your Own Adventure' written in Emacs Org Mode
I authored and developed an interactive children's book about entrepreneurship and money management. The journey started with Twinery, the open-source tool for making interactive fiction, discovered right here on HN. The tool kindled memories of reading CYOA style books when I was a kid, and I thought the format would be awesome for writing a story my kids could follow along, incorporating play money to learn about transactions as they occurred in the story.<p>Twinery is a fantastic tool, and I used it to layout the story map. I really wanted to write the content of the story in Emacs and Org Mode however. Thankfully, Twinery provided the ability to write custom Story Formats that defined how a story was exported. I wrote a Story Format called Twiorg that would export the Twinery file to an Org file and then a Org export backend (ox-twee) to do the reverse. With these tools, I could go back and forth between Emacs and Twinery for authoring the story.<p>The project snowballed and I ended up with the book in digital and physical book formats. The Web Book is created using another Org export backend.<p>Ten Dollar Adventure: <a href="https://tendollaradventure.com" rel="nofollow">https://tendollaradventure.com</a><p>Sample the Web Book (one complete storyline/adventure): <a href="https://tendollaradventure.com/sample/" rel="nofollow">https://tendollaradventure.com/sample/</a><p>I couldn't muster the effort to write a special org export backend for the physical books unfortunately and used a commercial editor to format these.<p>Twiorg: <a href="https://github.com/danishec/twiorg">https://github.com/danishec/twiorg</a><p>ox-twee: <a href="https://github.com/danishec/ox-twee">https://github.com/danishec/ox-twee</a><p>Previous HN post on writing the transaction logic using an LLM in Emacs: <a href="https://blog.tendollaradventure.com/automating-story-logic-with-llms/" rel="nofollow">https://blog.tendollaradventure.com/automating-story-logic-w...</a><p>Twinery 2: <<a href="https://twinery.org/" rel="nofollow">https://twinery.org/</a>> and discussion on HN: <a href="https://news.ycombinator.com/item?id=32788965">https://news.ycombinator.com/item?id=32788965</a>
Show HN: A 'Choose Your Own Adventure' written in Emacs Org Mode
I authored and developed an interactive children's book about entrepreneurship and money management. The journey started with Twinery, the open-source tool for making interactive fiction, discovered right here on HN. The tool kindled memories of reading CYOA style books when I was a kid, and I thought the format would be awesome for writing a story my kids could follow along, incorporating play money to learn about transactions as they occurred in the story.<p>Twinery is a fantastic tool, and I used it to layout the story map. I really wanted to write the content of the story in Emacs and Org Mode however. Thankfully, Twinery provided the ability to write custom Story Formats that defined how a story was exported. I wrote a Story Format called Twiorg that would export the Twinery file to an Org file and then a Org export backend (ox-twee) to do the reverse. With these tools, I could go back and forth between Emacs and Twinery for authoring the story.<p>The project snowballed and I ended up with the book in digital and physical book formats. The Web Book is created using another Org export backend.<p>Ten Dollar Adventure: <a href="https://tendollaradventure.com" rel="nofollow">https://tendollaradventure.com</a><p>Sample the Web Book (one complete storyline/adventure): <a href="https://tendollaradventure.com/sample/" rel="nofollow">https://tendollaradventure.com/sample/</a><p>I couldn't muster the effort to write a special org export backend for the physical books unfortunately and used a commercial editor to format these.<p>Twiorg: <a href="https://github.com/danishec/twiorg">https://github.com/danishec/twiorg</a><p>ox-twee: <a href="https://github.com/danishec/ox-twee">https://github.com/danishec/ox-twee</a><p>Previous HN post on writing the transaction logic using an LLM in Emacs: <a href="https://blog.tendollaradventure.com/automating-story-logic-with-llms/" rel="nofollow">https://blog.tendollaradventure.com/automating-story-logic-w...</a><p>Twinery 2: <<a href="https://twinery.org/" rel="nofollow">https://twinery.org/</a>> and discussion on HN: <a href="https://news.ycombinator.com/item?id=32788965">https://news.ycombinator.com/item?id=32788965</a>
Show HN: A 'Choose Your Own Adventure' written in Emacs Org Mode
I authored and developed an interactive children's book about entrepreneurship and money management. The journey started with Twinery, the open-source tool for making interactive fiction, discovered right here on HN. The tool kindled memories of reading CYOA style books when I was a kid, and I thought the format would be awesome for writing a story my kids could follow along, incorporating play money to learn about transactions as they occurred in the story.<p>Twinery is a fantastic tool, and I used it to layout the story map. I really wanted to write the content of the story in Emacs and Org Mode however. Thankfully, Twinery provided the ability to write custom Story Formats that defined how a story was exported. I wrote a Story Format called Twiorg that would export the Twinery file to an Org file and then a Org export backend (ox-twee) to do the reverse. With these tools, I could go back and forth between Emacs and Twinery for authoring the story.<p>The project snowballed and I ended up with the book in digital and physical book formats. The Web Book is created using another Org export backend.<p>Ten Dollar Adventure: <a href="https://tendollaradventure.com" rel="nofollow">https://tendollaradventure.com</a><p>Sample the Web Book (one complete storyline/adventure): <a href="https://tendollaradventure.com/sample/" rel="nofollow">https://tendollaradventure.com/sample/</a><p>I couldn't muster the effort to write a special org export backend for the physical books unfortunately and used a commercial editor to format these.<p>Twiorg: <a href="https://github.com/danishec/twiorg">https://github.com/danishec/twiorg</a><p>ox-twee: <a href="https://github.com/danishec/ox-twee">https://github.com/danishec/ox-twee</a><p>Previous HN post on writing the transaction logic using an LLM in Emacs: <a href="https://blog.tendollaradventure.com/automating-story-logic-with-llms/" rel="nofollow">https://blog.tendollaradventure.com/automating-story-logic-w...</a><p>Twinery 2: <<a href="https://twinery.org/" rel="nofollow">https://twinery.org/</a>> and discussion on HN: <a href="https://news.ycombinator.com/item?id=32788965">https://news.ycombinator.com/item?id=32788965</a>
Show HN: Improving search ranking with chess Elo scores
Hello HN,<p>I'm Ghita, co-founder of ZeroEntropy (YC W25). We build high accuracy search infrastructure for RAG and AI Agents.<p>We just released two new state-of-the-art rerankers zerank-1, and zerank-1-small. One of them is fully open-source under Apache 2.0.<p>We trained those models using a novel Elo score inspired pipeline which we describe in detail in the blog attached. In a nutshell, here is an outline of the training steps:
* Collect soft preferences between pairs of documents using an ensemble of LLMs.
* Fit an ELO-style rating system (Bradley-Terry) to turn pairwise comparisons into absolute per-document scores.
* Normalize relevance scores across queries using a bias correction step, modeled using cross-query comparisons and solved with MLE.<p>You can try the models either through our API (<a href="https://docs.zeroentropy.dev/models">https://docs.zeroentropy.dev/models</a>), or via HuggingFace (<a href="https://huggingface.co/zeroentropy/zerank-1-small" rel="nofollow">https://huggingface.co/zeroentropy/zerank-1-small</a>).<p>We would love this community's feedback on the models, and the training approach. A full technical report is also going to be released soon.<p>Thank you!
Show HN: Improving search ranking with chess Elo scores
Hello HN,<p>I'm Ghita, co-founder of ZeroEntropy (YC W25). We build high accuracy search infrastructure for RAG and AI Agents.<p>We just released two new state-of-the-art rerankers zerank-1, and zerank-1-small. One of them is fully open-source under Apache 2.0.<p>We trained those models using a novel Elo score inspired pipeline which we describe in detail in the blog attached. In a nutshell, here is an outline of the training steps:
* Collect soft preferences between pairs of documents using an ensemble of LLMs.
* Fit an ELO-style rating system (Bradley-Terry) to turn pairwise comparisons into absolute per-document scores.
* Normalize relevance scores across queries using a bias correction step, modeled using cross-query comparisons and solved with MLE.<p>You can try the models either through our API (<a href="https://docs.zeroentropy.dev/models">https://docs.zeroentropy.dev/models</a>), or via HuggingFace (<a href="https://huggingface.co/zeroentropy/zerank-1-small" rel="nofollow">https://huggingface.co/zeroentropy/zerank-1-small</a>).<p>We would love this community's feedback on the models, and the training approach. A full technical report is also going to be released soon.<p>Thank you!
Show HN: Improving search ranking with chess Elo scores
Hello HN,<p>I'm Ghita, co-founder of ZeroEntropy (YC W25). We build high accuracy search infrastructure for RAG and AI Agents.<p>We just released two new state-of-the-art rerankers zerank-1, and zerank-1-small. One of them is fully open-source under Apache 2.0.<p>We trained those models using a novel Elo score inspired pipeline which we describe in detail in the blog attached. In a nutshell, here is an outline of the training steps:
* Collect soft preferences between pairs of documents using an ensemble of LLMs.
* Fit an ELO-style rating system (Bradley-Terry) to turn pairwise comparisons into absolute per-document scores.
* Normalize relevance scores across queries using a bias correction step, modeled using cross-query comparisons and solved with MLE.<p>You can try the models either through our API (<a href="https://docs.zeroentropy.dev/models">https://docs.zeroentropy.dev/models</a>), or via HuggingFace (<a href="https://huggingface.co/zeroentropy/zerank-1-small" rel="nofollow">https://huggingface.co/zeroentropy/zerank-1-small</a>).<p>We would love this community's feedback on the models, and the training approach. A full technical report is also going to be released soon.<p>Thank you!
Show HN: Improving search ranking with chess Elo scores
Hello HN,<p>I'm Ghita, co-founder of ZeroEntropy (YC W25). We build high accuracy search infrastructure for RAG and AI Agents.<p>We just released two new state-of-the-art rerankers zerank-1, and zerank-1-small. One of them is fully open-source under Apache 2.0.<p>We trained those models using a novel Elo score inspired pipeline which we describe in detail in the blog attached. In a nutshell, here is an outline of the training steps:
* Collect soft preferences between pairs of documents using an ensemble of LLMs.
* Fit an ELO-style rating system (Bradley-Terry) to turn pairwise comparisons into absolute per-document scores.
* Normalize relevance scores across queries using a bias correction step, modeled using cross-query comparisons and solved with MLE.<p>You can try the models either through our API (<a href="https://docs.zeroentropy.dev/models">https://docs.zeroentropy.dev/models</a>), or via HuggingFace (<a href="https://huggingface.co/zeroentropy/zerank-1-small" rel="nofollow">https://huggingface.co/zeroentropy/zerank-1-small</a>).<p>We would love this community's feedback on the models, and the training approach. A full technical report is also going to be released soon.<p>Thank you!
Show HN: Improving search ranking with chess Elo scores
Hello HN,<p>I'm Ghita, co-founder of ZeroEntropy (YC W25). We build high accuracy search infrastructure for RAG and AI Agents.<p>We just released two new state-of-the-art rerankers zerank-1, and zerank-1-small. One of them is fully open-source under Apache 2.0.<p>We trained those models using a novel Elo score inspired pipeline which we describe in detail in the blog attached. In a nutshell, here is an outline of the training steps:
* Collect soft preferences between pairs of documents using an ensemble of LLMs.
* Fit an ELO-style rating system (Bradley-Terry) to turn pairwise comparisons into absolute per-document scores.
* Normalize relevance scores across queries using a bias correction step, modeled using cross-query comparisons and solved with MLE.<p>You can try the models either through our API (<a href="https://docs.zeroentropy.dev/models">https://docs.zeroentropy.dev/models</a>), or via HuggingFace (<a href="https://huggingface.co/zeroentropy/zerank-1-small" rel="nofollow">https://huggingface.co/zeroentropy/zerank-1-small</a>).<p>We would love this community's feedback on the models, and the training approach. A full technical report is also going to be released soon.<p>Thank you!