Cerebras Systems Raises $1.1B Series G at $8.1B Valuation
32 by fcpguru | 8 comments on Hacker News.
Tuesday, 30 September 2025
Monday, 29 September 2025
Trump's Boast Of A Golden White House 'Freak Out' Backfires Spectacularly
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Sunday, 28 September 2025
Saturday, 27 September 2025
Honda sees surprising model surge to top of sales charts: 'A great addition to our lineup'
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New top story on Hacker News: Americans Are Using PTO to Sleep, Not for Vacation–Report
Americans Are Using PTO to Sleep, Not for Vacation–Report
21 by randycupertino | 10 comments on Hacker News.
21 by randycupertino | 10 comments on Hacker News.
Friday, 26 September 2025
They said it: Leaders at the UN, in their own words
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Thursday, 25 September 2025
New top story on Hacker News: Starbucks: Location closures and elimination of roles
Starbucks: Location closures and elimination of roles
28 by ChrisArchitect | 23 comments on Hacker News.
28 by ChrisArchitect | 23 comments on Hacker News.
Wednesday, 24 September 2025
Cathie Wood says the AI race has already shrunk to the ‘Big 4’ key players, and it could soon drop to 2
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Tuesday, 23 September 2025
Bay Area biotech company lays off half its workers a year after raising $325M
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Monday, 22 September 2025
Turning Point USA Spokesperson Says Charlie Kirk Didn’t Have an ‘Exit Wound’: A ‘Miracle’
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Sunday, 21 September 2025
Hungry Mama Bear and Cubs Steal Food From Back of Truck Like It’s a Smorgasbord
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Saturday, 20 September 2025
Congressman Calmly Explains There Are "Entities" Coming From “Five or Six Deepwater Areas”
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Friday, 19 September 2025
Homeowner stunned after checking security footage of driveway: 'At first I thought it was the neighbor's dog'
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I've built up 1,000 hours of PTO — but now my company wants to pay me out at 35% of my wage. Is that fair?
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Thursday, 18 September 2025
New top story on Hacker News: Show HN: One prompt generates an app with its own database
Show HN: One prompt generates an app with its own database
7 by stopachka | 6 comments on Hacker News.
Hey HN, manyminiapps is the world first massively multiplayer online mini app builder (MMOMA) *Here’s what it does:* You load the page. You write 1 prompt and you get a mini app back in under 2 minutes. There’s no sign up, and you can see what everyone’s creating in real-time! Each mini app comes with it’s own database and backend, so you can build shareable apps that save data. *What’s different* There are a lot of app builders that promise you’ll build production software for others. But we think true production software can take a long time to get right. Even if you don’t need to program there’s a lot of work involved. What if we turned the promise around? Instead of “you vibe code software companies”, it’s “you build fun software for yourself”. If you cut the problem right, LLMs as they are today can already deliver personal software. manyminiapps is meant to be an experiment to demonstrate this. You may wonder: do you really need personal software? We’re not 100% sure, but it’s definitely an interesting question. Using manyminiapps so far has been surprising! We thought our friends would just try to build the common todo app, but instead we found them building wedding planners, chord progression helpers, inspiration lists, and retro games. *How it works* Instead of spinning up VMs or separate instances per app, we built a multi-tenant graph database on top of 1 large Postgres instance. All databases live under 1 table, on an EAV table (entity, attribute, value). This makes it so creating an “app” is as light as creating a new row. If you have heard of EAV tables before, you may know that most Postgres experts will tell you not to use them. Postgres needs to know statistics in order to make efficient query plans. But when you use EAV tables, Postgres can no longer get good statistics. This is usually a bad idea. But we thought it was worth solving to get a multi-tenant relational database. To solve this problem we started saving our own statistics in a custom table. We use count-min sketches to keep stats about each app’s columns. When a user writes a query, we figure out the indexes to use and get pg_hint_plan to tell Postgres what to do. *What we’ve learned so far* We’ve tried both GPT 5, Claude Opus, and Claude Sonnet for LLM providers. GPT 5 followed the instructions the best amongst the models. Even if you told it a completely nonsensical prompt (like “absda”, it would follow the system prompt and make an app for you. But GPT 5 was also the “most lazy”. The apps that came out tended to feel too simple, with little UI detail. Both Claude Opus and Sonnet were less good at following instructions. Even when we told them to return just the code, they wanted to returned markdown blocks. But, after parsing through those blocks, the resulting apps felt much better. To our surprise, we didn’t notice a difference in quality from Opus and Sonnet. Both models did well, with perhaps Sonnet following instructions more closely. To get good results we iterated on prompts. We initially tried giving point-by-point instructions, but found that a prompt with a full example tended to do better. Here’s what we landed on: https://gist.github.com/stopachka/a6b07e1e6daeb85fa7c9555d8f... Let us know what you think, and hope you have fun : )
7 by stopachka | 6 comments on Hacker News.
Hey HN, manyminiapps is the world first massively multiplayer online mini app builder (MMOMA) *Here’s what it does:* You load the page. You write 1 prompt and you get a mini app back in under 2 minutes. There’s no sign up, and you can see what everyone’s creating in real-time! Each mini app comes with it’s own database and backend, so you can build shareable apps that save data. *What’s different* There are a lot of app builders that promise you’ll build production software for others. But we think true production software can take a long time to get right. Even if you don’t need to program there’s a lot of work involved. What if we turned the promise around? Instead of “you vibe code software companies”, it’s “you build fun software for yourself”. If you cut the problem right, LLMs as they are today can already deliver personal software. manyminiapps is meant to be an experiment to demonstrate this. You may wonder: do you really need personal software? We’re not 100% sure, but it’s definitely an interesting question. Using manyminiapps so far has been surprising! We thought our friends would just try to build the common todo app, but instead we found them building wedding planners, chord progression helpers, inspiration lists, and retro games. *How it works* Instead of spinning up VMs or separate instances per app, we built a multi-tenant graph database on top of 1 large Postgres instance. All databases live under 1 table, on an EAV table (entity, attribute, value). This makes it so creating an “app” is as light as creating a new row. If you have heard of EAV tables before, you may know that most Postgres experts will tell you not to use them. Postgres needs to know statistics in order to make efficient query plans. But when you use EAV tables, Postgres can no longer get good statistics. This is usually a bad idea. But we thought it was worth solving to get a multi-tenant relational database. To solve this problem we started saving our own statistics in a custom table. We use count-min sketches to keep stats about each app’s columns. When a user writes a query, we figure out the indexes to use and get pg_hint_plan to tell Postgres what to do. *What we’ve learned so far* We’ve tried both GPT 5, Claude Opus, and Claude Sonnet for LLM providers. GPT 5 followed the instructions the best amongst the models. Even if you told it a completely nonsensical prompt (like “absda”, it would follow the system prompt and make an app for you. But GPT 5 was also the “most lazy”. The apps that came out tended to feel too simple, with little UI detail. Both Claude Opus and Sonnet were less good at following instructions. Even when we told them to return just the code, they wanted to returned markdown blocks. But, after parsing through those blocks, the resulting apps felt much better. To our surprise, we didn’t notice a difference in quality from Opus and Sonnet. Both models did well, with perhaps Sonnet following instructions more closely. To get good results we iterated on prompts. We initially tried giving point-by-point instructions, but found that a prompt with a full example tended to do better. Here’s what we landed on: https://gist.github.com/stopachka/a6b07e1e6daeb85fa7c9555d8f... Let us know what you think, and hope you have fun : )
Wednesday, 17 September 2025
Tuesday, 16 September 2025
Nvidia suffers a major blow from China
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Monday, 15 September 2025
New top story on Hacker News: Show HN: AI-powered web service combining FastAPI, Pydantic-AI, and MCP servers
Show HN: AI-powered web service combining FastAPI, Pydantic-AI, and MCP servers
5 by Aherontas | 1 comments on Hacker News.
Hey all! I recently gave a workshop talk at PyCon Greece 2025 about building production-ready agent systems. To check the workshop, I put together a demo repo: (I will add the slides too soon in my blog: https://ift.tt/uLm4DTl ) https://ift.tt/kmQHnLC... The idea was to show how multiple AI agents can collaborate using FastAPI + Pydantic-AI, with protocols like MCP (Model Context Protocol) and A2A (Agent-to-Agent) for safe communication and orchestration. Features: - Multiple agents running in containers - MCP servers (Brave search, GitHub, filesystem, etc.) as tools - A2A communication between services - Minimal UI for experimentation for Tech Trend - repo analysis I built this repo because most agent frameworks look great in isolated demos, but fall apart when you try to glue agents together into a real application. My goal was to help people experiment with these patterns and move closer to real-world use cases. It’s not production-grade, but would love feedback, criticism, or war stories from anyone who’s tried building actual multi-agent systems. Big questions: Do you think agent-to-agent protocols like MCP/A2A will stick? Or will the future be mostly single powerful LLMs with plugin stacks? Thanks — excited to hear what the HN crowd thinks!
5 by Aherontas | 1 comments on Hacker News.
Hey all! I recently gave a workshop talk at PyCon Greece 2025 about building production-ready agent systems. To check the workshop, I put together a demo repo: (I will add the slides too soon in my blog: https://ift.tt/uLm4DTl ) https://ift.tt/kmQHnLC... The idea was to show how multiple AI agents can collaborate using FastAPI + Pydantic-AI, with protocols like MCP (Model Context Protocol) and A2A (Agent-to-Agent) for safe communication and orchestration. Features: - Multiple agents running in containers - MCP servers (Brave search, GitHub, filesystem, etc.) as tools - A2A communication between services - Minimal UI for experimentation for Tech Trend - repo analysis I built this repo because most agent frameworks look great in isolated demos, but fall apart when you try to glue agents together into a real application. My goal was to help people experiment with these patterns and move closer to real-world use cases. It’s not production-grade, but would love feedback, criticism, or war stories from anyone who’s tried building actual multi-agent systems. Big questions: Do you think agent-to-agent protocols like MCP/A2A will stick? Or will the future be mostly single powerful LLMs with plugin stacks? Thanks — excited to hear what the HN crowd thinks!
Sunday, 14 September 2025
Lawmakers push forward on controversial ban of popular retail item: 'I don't want children to be able to access these'
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Saturday, 13 September 2025
Second crash happens on 32 Road in less than 24 hours
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Friday, 12 September 2025
Thursday, 11 September 2025
Wednesday, 10 September 2025
Tuesday, 9 September 2025
Monday, 8 September 2025
Sunday, 7 September 2025
Recent study finds 'garbage lasagnas' forming in open landfills across US release staggering amount of air pollution: 'Something has gone awry'
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Saturday, 6 September 2025
Friday, 5 September 2025
New top story on Hacker News: Show HN: Open-sourcing our text-to-CAD app
Show HN: Open-sourcing our text-to-CAD app
4 by zachdive | 0 comments on Hacker News.
Hey HN! I'm Zach from Adam ( https://adam.new/ ). We’re building an AI co-pilot for mechanical CAD software. As part of our broader research, we built a browser-based Text-to-CAD app ( https://ift.tt/xDnmoWG ) and are now open sourcing it. This is a React SPA with a Supabase backend. What it does: * Generates parametric 3D models from natural language descriptions, with support for both text prompts and image references * Outputs OpenSCAD code with automatically extracted parameters that surface as interactive sliders for instant dimension tweaking * Exports as .STL or .SCAD Under the hood: * Separate agents for conversation and code generation; simple parameter tweaks bypass AI entirely using deterministic regex-based updates * Runs fully in-browser by compiling OpenSCAD to WebAssembly and integrating Three.js with React Three Fiber for 3D rendering * Supports BOSL, BOSL2, MCAD libraries and custom font support (Geist) for text in models We’ve seen many developers trying to replicate this kind of functionality, so we’re releasing this to give the community a solid foundation to build on. Future improvements: * Expand geometry support - Move beyond CSG primitives to support curved surfaces, fillets, lofts, and constraint-driven modeling through CadQuery/Build123D * Better spatial context - UI for face/edge selection and viewport image integration to give LLMs spatial understanding * Enhanced capabilities - RAG on documentation and integration with more OpenSCAD libraries for features like proper threading You can clone the repo and run it locally! Contributions are welcome, and we’ll keep merging PRs as they come in.
4 by zachdive | 0 comments on Hacker News.
Hey HN! I'm Zach from Adam ( https://adam.new/ ). We’re building an AI co-pilot for mechanical CAD software. As part of our broader research, we built a browser-based Text-to-CAD app ( https://ift.tt/xDnmoWG ) and are now open sourcing it. This is a React SPA with a Supabase backend. What it does: * Generates parametric 3D models from natural language descriptions, with support for both text prompts and image references * Outputs OpenSCAD code with automatically extracted parameters that surface as interactive sliders for instant dimension tweaking * Exports as .STL or .SCAD Under the hood: * Separate agents for conversation and code generation; simple parameter tweaks bypass AI entirely using deterministic regex-based updates * Runs fully in-browser by compiling OpenSCAD to WebAssembly and integrating Three.js with React Three Fiber for 3D rendering * Supports BOSL, BOSL2, MCAD libraries and custom font support (Geist) for text in models We’ve seen many developers trying to replicate this kind of functionality, so we’re releasing this to give the community a solid foundation to build on. Future improvements: * Expand geometry support - Move beyond CSG primitives to support curved surfaces, fillets, lofts, and constraint-driven modeling through CadQuery/Build123D * Better spatial context - UI for face/edge selection and viewport image integration to give LLMs spatial understanding * Enhanced capabilities - RAG on documentation and integration with more OpenSCAD libraries for features like proper threading You can clone the repo and run it locally! Contributions are welcome, and we’ll keep merging PRs as they come in.
Thursday, 4 September 2025
New top story on Hacker News: A high schooler writes about AI tools in the classroom
A high schooler writes about AI tools in the classroom
76 by dougb5 | 69 comments on Hacker News.
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76 by dougb5 | 69 comments on Hacker News.
https://ift.tt/caLZkoM
Wednesday, 3 September 2025
New top story on Hacker News: Vector search on our codebase transformed our SDLC automation
Vector search on our codebase transformed our SDLC automation
9 by antonybrahin | 0 comments on Hacker News.
Hey HN, In software development, the process of turning a user story into detailed documentation and actionable tasks is critical. However, this manual process can often be a source of inconsistency and a significant time investment. I was driven to see if I could streamline and elevate it. I know this is a hot space, with big players like GitHub and Atlassian building integrated AI, and startups offering specialized platforms. My goal wasn't to compete with them, but to see what was possible by building a custom, "glass box" solution using the best tools for each part of the job, without being locked into a single ecosystem. What makes this approach different is the flexibility and full control. Instead of a pre-packaged product, this is a resilient workflow built on Power Automate, which acts as the orchestrator for a sequence of API calls: Five calls to the Gemini API for the core generation steps (requirements, tech spec, test strategy, etc.). One call to an Azure OpenAI model to create vector embeddings of our codebase. One call to Azure AI Search to perform the Retrieval-Augmented Generation (RAG). This was the key to getting context-aware, non-generic outputs. It reads our actual code to inform the technical spec and tasks. A bunch of direct calls to the Azure DevOps REST API (using a PAT) to create the wiki pages and work items, since the standard connectors were a bit limited. The biggest challenge was moving beyond simple prompts and engineering a resilient system. Forcing the final output into a rigid JSON schema instead of parsing text was a game-changer for reliability. The result is a system that saves us hours on every story and produces remarkably consistent, high-quality documentation and tasks. The full write-up with all the challenges, final prompts, and screenshots is in the linked blog post. I’m here to answer any questions. Would love to hear your feedback and ideas!
9 by antonybrahin | 0 comments on Hacker News.
Hey HN, In software development, the process of turning a user story into detailed documentation and actionable tasks is critical. However, this manual process can often be a source of inconsistency and a significant time investment. I was driven to see if I could streamline and elevate it. I know this is a hot space, with big players like GitHub and Atlassian building integrated AI, and startups offering specialized platforms. My goal wasn't to compete with them, but to see what was possible by building a custom, "glass box" solution using the best tools for each part of the job, without being locked into a single ecosystem. What makes this approach different is the flexibility and full control. Instead of a pre-packaged product, this is a resilient workflow built on Power Automate, which acts as the orchestrator for a sequence of API calls: Five calls to the Gemini API for the core generation steps (requirements, tech spec, test strategy, etc.). One call to an Azure OpenAI model to create vector embeddings of our codebase. One call to Azure AI Search to perform the Retrieval-Augmented Generation (RAG). This was the key to getting context-aware, non-generic outputs. It reads our actual code to inform the technical spec and tasks. A bunch of direct calls to the Azure DevOps REST API (using a PAT) to create the wiki pages and work items, since the standard connectors were a bit limited. The biggest challenge was moving beyond simple prompts and engineering a resilient system. Forcing the final output into a rigid JSON schema instead of parsing text was a game-changer for reliability. The result is a system that saves us hours on every story and produces remarkably consistent, high-quality documentation and tasks. The full write-up with all the challenges, final prompts, and screenshots is in the linked blog post. I’m here to answer any questions. Would love to hear your feedback and ideas!
Tuesday, 2 September 2025
Monday, 1 September 2025
Explainer-The slow train from North Korea: How Kim Jong Un travels to China
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Thousands join rallies against mass immigration across Australia
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