Princeton NuEnergy's battery recycling tech recovers 97% of lithium-ion material
6 by jbotz | 0 comments on Hacker News.
Saturday, 16 August 2025
Friday, 15 August 2025
Thursday, 14 August 2025
New top story on Hacker News: Show HN: Modelence – Supabase for MongoDB
Show HN: Modelence – Supabase for MongoDB
12 by artahian | 0 comments on Hacker News.
Hi all, Aram and Eduard here - authors of Modelence ( https://ift.tt/I7lFBxH ), an all-in-one backend platform for teams that love TypeScript + MongoDB. Think Supabase, but for MongoDB: auth, cron jobs, email, monitoring, without glue code before you can ship. As Karpathy (and many of us) noted, getting from prototype to production is mostly painful integration work. The pieces exist, but stitching them together reliably is the hard part: https://ift.tt/YfbCUZc . YC AI Startup School talk about this - https://www.youtube.com/watch?feature=shared&t=1940&v=LCEmiR... We intend to fill those gaps! What you get out of the box: - Authentication / user management - Database - Email integration (3rd party, but things like user verification emails work out of the box) - AI integration - Cron jobs - Monitoring / Telemetry - Configs & secrets - Analytics (coming soon) - File uploads (coming soon) How it runs: A Node.js backend with MongoDB. It's frontend-agnostic, so you can use our minimal Vite + React starter or drop Modelence behind an existing Next.js (or any) frontend. We're also building a managed cloud, similar to what Vercel is for Next.js, except Modelence focuses on the backend instead of the frontend (Vercel is great for content sites like landing pages, blogs, etc, but things like persistent connections and complex backend logic outgrow it quickly). You can find a quick demo here: https://www.youtube.com/watch?v=S4f22FyPpI8 We're looking for early users (especially TS teams on MongoDB). Tell us what's missing, what's confusing, and what you'd want before trusting this in prod. Happy to answer anything!
12 by artahian | 0 comments on Hacker News.
Hi all, Aram and Eduard here - authors of Modelence ( https://ift.tt/I7lFBxH ), an all-in-one backend platform for teams that love TypeScript + MongoDB. Think Supabase, but for MongoDB: auth, cron jobs, email, monitoring, without glue code before you can ship. As Karpathy (and many of us) noted, getting from prototype to production is mostly painful integration work. The pieces exist, but stitching them together reliably is the hard part: https://ift.tt/YfbCUZc . YC AI Startup School talk about this - https://www.youtube.com/watch?feature=shared&t=1940&v=LCEmiR... We intend to fill those gaps! What you get out of the box: - Authentication / user management - Database - Email integration (3rd party, but things like user verification emails work out of the box) - AI integration - Cron jobs - Monitoring / Telemetry - Configs & secrets - Analytics (coming soon) - File uploads (coming soon) How it runs: A Node.js backend with MongoDB. It's frontend-agnostic, so you can use our minimal Vite + React starter or drop Modelence behind an existing Next.js (or any) frontend. We're also building a managed cloud, similar to what Vercel is for Next.js, except Modelence focuses on the backend instead of the frontend (Vercel is great for content sites like landing pages, blogs, etc, but things like persistent connections and complex backend logic outgrow it quickly). You can find a quick demo here: https://www.youtube.com/watch?v=S4f22FyPpI8 We're looking for early users (especially TS teams on MongoDB). Tell us what's missing, what's confusing, and what you'd want before trusting this in prod. Happy to answer anything!
Wednesday, 13 August 2025
Tuesday, 12 August 2025
Monday, 11 August 2025
Sunday, 10 August 2025
You'll Never Believe What Microsoft CEO Satya Nadella Said About Quantum Computing
from Yahoo News - Latest News & Headlines https://ift.tt/TiCm2un
New top story on Hacker News: Show HN: Llmswap – Python package to reduce LLM API costs by 50-90% with caching
Show HN: Llmswap – Python package to reduce LLM API costs by 50-90% with caching
8 by sreenathmenon | 1 comments on Hacker News.
I built llmswap to solve a problem I kept hitting in hackathons - burning through API credits while testing the same prompts repeatedly during development. It's a simple Python package that provides a unified interface for OpenAI, Anthropic, Google Gemini, and local models (Ollama), with built-in response caching that can cut API costs by 50-90%. Key features: - Intelligent caching with TTL and memory limits - Context-aware caching for multi-user apps - Auto-fallback between providers when one fails - Zero configuration - works with environment variables from llmswap import LLMClient client = LLMClient(cache_enabled=True) response = client.query("Explain quantum computing") # Second identical query returns from cache instantly (free) The caching is disabled by default for security. When enabled, it's thread-safe and includes context isolation for multi-user applications. Built this from components of a hackathon project. Already at 2.2k downloads on PyPI. Hope it helps others save on API costs during development. GitHub: https://ift.tt/dKtHujf PyPI: https://ift.tt/IML0ETw
8 by sreenathmenon | 1 comments on Hacker News.
I built llmswap to solve a problem I kept hitting in hackathons - burning through API credits while testing the same prompts repeatedly during development. It's a simple Python package that provides a unified interface for OpenAI, Anthropic, Google Gemini, and local models (Ollama), with built-in response caching that can cut API costs by 50-90%. Key features: - Intelligent caching with TTL and memory limits - Context-aware caching for multi-user apps - Auto-fallback between providers when one fails - Zero configuration - works with environment variables from llmswap import LLMClient client = LLMClient(cache_enabled=True) response = client.query("Explain quantum computing") # Second identical query returns from cache instantly (free) The caching is disabled by default for security. When enabled, it's thread-safe and includes context isolation for multi-user applications. Built this from components of a hackathon project. Already at 2.2k downloads on PyPI. Hope it helps others save on API costs during development. GitHub: https://ift.tt/dKtHujf PyPI: https://ift.tt/IML0ETw
Saturday, 9 August 2025
New top story on Hacker News: Ask HN: What Toolchains Are People Using for Desktop App Development in 2025?
Ask HN: What Toolchains Are People Using for Desktop App Development in 2025?
16 by lincoln20xx | 16 comments on Hacker News.
I recently came across a comment [0] here that highlighted the challenges of building native desktop apps with LLMs. The commenter pointed out how scarce training resources have become—few blog posts, tutorials, or open-source projects exist compared to web or mobile development. They also noted that while desktop app development was a solid career path in the 90s, it's now seen as a dead end for most, outside of big players like Microsoft or Adobe. This got me thinking: My own experience with desktop development dates back to the late 90s using Turbo Pascal 6 in Delphi, and I'm out of the loop on modern practices. With the evolving landscape, I'm curious about what tools and workflows developers are actually using today. Some questions to spark discussion: - What programming languages and frameworks are popular for desktop apps? - Are there any go-to IDEs, build tools, or libraries that make development easier? - Do the above answers change if you care about code performance or efficiency (whatever that means to you)? - Is native desktop app development still viable as a career, or are most greenfield projects shifting to web-based alternatives? I'd love to hear from folks with recent experience—success stories, pitfalls, and recommendations would be great. Thanks in advance for sharing! [0] https://ift.tt/gQHwshM
16 by lincoln20xx | 16 comments on Hacker News.
I recently came across a comment [0] here that highlighted the challenges of building native desktop apps with LLMs. The commenter pointed out how scarce training resources have become—few blog posts, tutorials, or open-source projects exist compared to web or mobile development. They also noted that while desktop app development was a solid career path in the 90s, it's now seen as a dead end for most, outside of big players like Microsoft or Adobe. This got me thinking: My own experience with desktop development dates back to the late 90s using Turbo Pascal 6 in Delphi, and I'm out of the loop on modern practices. With the evolving landscape, I'm curious about what tools and workflows developers are actually using today. Some questions to spark discussion: - What programming languages and frameworks are popular for desktop apps? - Are there any go-to IDEs, build tools, or libraries that make development easier? - Do the above answers change if you care about code performance or efficiency (whatever that means to you)? - Is native desktop app development still viable as a career, or are most greenfield projects shifting to web-based alternatives? I'd love to hear from folks with recent experience—success stories, pitfalls, and recommendations would be great. Thanks in advance for sharing! [0] https://ift.tt/gQHwshM
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