The first electric Ferrari is already this year’s most divisive car. We asked three Italian auto industry professionals to explain where the EV’s design makes sense, and where it doesn’t add up.

I've been working on a side project called WEBDEVPACK - a collection of free web-based tools for developers, designers, and generally anyone working online. The idea is simple: lightweight tools that help us solve everyday problems. Check it out at webdevpack.com submitted by /u/ivopetkov [link] [留言]

I'm working on a self-hosted search service called Hister with the goal to reduce dependence on online search engines. Hister is a full text indexer for websites and local files which automatically saves all the visited pages rendered by your browser. It provides a flexible web (and terminal) search interface & query language to explore saved content with ease or quickly fall back to traditional search engines. I've been using it for a few months and as my local index is growing I can avoid opening google/duckduckgo/kagi - and even websites listed in results - more and more frequently. The initial reception is overwhelmingly positive with already more than 30 contributors and hundreds of contributions - perhaps you can find it useful as well. (Or at least have some constructive criticism =]) The code is AGPLv3 licensed, available at https://github.com/asciimoo/hister , read-only demo: https://demo.hister.org/ , website: https://hister.org/ submitted by /u/asciimoo [link] [留言]

Hello! I am Axel and I’ve been building Kanto, a small forum platform for niche communities. The main idea is that communities can allow normal posting or optional anonymous posting which is fully secure and private, while still requiring accounts behind the scenes. Users can create communities, start threads, reply, and browse communities around specific interests! :) I’m trying to keep it simple and privacy-friendly: no ads, no selling user data, and a cleaner experience than huge social platforms, not like the big american social media companies that sell your data. https://kanto.forum submitted by /u/axeeeeeel- [link] [留言]

Hey r/ML , I spent the last few months building a tool that hooks into PyTorch training loops to automatically detect and localize failures (vanishing gradients, exploding gradients, data anomalies). Along the way, I learned some things about training failure diagnosis that might be useful even if you never use the tool. The key insight: most training failures are local, not global When your loss spikes or vanishes, the natural instinct is to look at the loss curve. But the loss is a global aggregate — it tells you something went wrong, but not where . In my testing across hundreds of synthetic failure scenarios, the actual root cause is almost always localized to a specific layer at a specific step : Vanishing gradients: the failure starts at the deepest layer with saturated activations, then propagates backward Exploding gradients: the failure starts at the layer with the highest gradient norm, then propagates forward Data anomalies: the failure starts at the input layer, then corrupts everything downstream The trick is to monitor per-layer gradient norms and detect transitions (healthy → vanishing), not absolute values. What actually matters in gradient monitoring Most people monitor: - Loss over time (too global) - Gradient histograms (too noisy, too much data) - Weight norms (slow to change, lagging indicator) What I found works best: - Gradient norm transitions : "Linear_3 went from healthy (0.12) to vanishing (0.00003) at step 47" - First occurrence tracking : which layer failed first (this is usually the root cause) - Activation regime shifts : when activations go from normal to saturated/dead This is basically what NeuralDBG does under the hood — I open-sourced it recently and it's on PyPI ( pip install neuraldbg ) if anyone wants to try it. The key design choice was to extract semantic events (transitions) rather than raw tensors — this makes the output small enough to reason about. Practical takeaway you can use today Even without any tool, you can add th

This is a submission for the Hermes Agent Challenge: Write About Hermes Agent Hermes Agent...

https://preview.redd.it/035k5k1tl84h1.png?width=1122&format=png&auto=webp&s=459c430ea4a4b3fc667bc3f2e72ab47d8a380aa2 I asked gemini to expand my prompt for a video generator, but he had other plans for me. EDIT: Forgot to mention, using the PRO model, after around 15 seconds of him literally thinking and writing "expanding the prompt", he started generating the video... submitted by /u/ObjectiveOrchid5344 [link] [留言]

The activewear giant has used chemical recycling to make jersey for 16 teams competing in the tournament. But the technique is unlikely to help solve fashion’s waste issue.

https://preview.redd.it/rbt6z0s1k84h1.jpg?width=1200&format=pjpg&auto=webp&s=87b4980b47fb45994978a1a719b5fbf3180c0860 – 6+ years of shooting, stitching, retouching, design and coding – Built solo. 0 funding, 0 sponsors, 0 ads. – Free for everyone – 80 places online 170 HD 360° panoramas (still only around 30% of what I've shot so far) – Map mode for browsing places across Tasmania, immersive journeys for larger full-screen 360° tours https://tas360.au submitted by /u/vla_dis [link] [留言]

https://lifeappbybabs.netlify.app I want to see if it's worth investing in and buying a domain name submitted by /u/Miserable_Spirit3319 [link] [留言]

A $0 multi-model decision agent: three LLMs debate, Hermes judges, and it learns who to trust.

TL;DR : Built a RAG pipeline, allows you to upload docs and connect via API/MCP to service. need feedback if you find it useful or confusing: https://rig-rag.vercel.app I got into the whole AI, embeddings, etc workflow for another project of mine and while pretty fascinating how this all ties together, I thought that it is perhaps also useful to others who don’t want to rebuild the whole pipeline from scratch every time. (also, competition seems pretty costly). It is pretty simple: Make account, make memory space, upload your stuff, configure how you want it retrieved, setup scoped API key, connect your AI to the hosted MCP → done. RigRAG lets you: upload / index documents search them via a simple HTTP API or MCP optionally configure retrieval settings like top-k, candidate count, chunking, etc. Currently entirely free to use since I would want to gather feedback first if it's worth pursuing further before I spend too much time into it. I’d really appreciate feedback on it, if something is unclear to setup, should be improved etc. I’m not trying to do a big launch yet so this is more of a “please try it, and tell me what sucks or if its good” thing. Here the link again: https://rig-rag.vercel.app And for the MCP: https://rig-rag.vercel.app/api/mcp submitted by /u/Odysseyan [link] [留言]

submitted by /u/Last_Success5724 [link] [留言]