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Autocoder.cc – The first full stack vibe coding tool

https://www.autocoder.cc/
1•Sharon_Q•1m ago•1 comments

Elon Musk's Tesla to open first India store in Mumbai on July 15

https://www.thehindu.com/business/elon-musks-tesla-to-open-first-india-store-in-mumbai-on-july-15/article69798935.ece
1•Bluestein•1m ago•0 comments

METR's AI productivity study is good

https://www.seangoedecke.com/impact-of-ai-study/
1•ingve•3m ago•0 comments

Transition to using 16 KB page sizes for Android apps and games

https://android-developers.googleblog.com/2025/07/transition-to-16-kb-page-sizes-android-apps-games-android-studio.html
1•ingve•6m ago•0 comments

The remarkable rise of an Australian deputy mayor to a plum job

https://www.smh.com.au/national/the-remarkable-rise-of-an-australian-deputy-mayor-to-a-plum-trump-job-20250711-p5me60.html
1•KnuthIsGod•6m ago•0 comments

Sipgate discovers null-pointer-dereference in Mediatek VoLTE stack firmware

https://www.sipgate.de/blog/sipgate-discovers-null-pointer-dereference-in-mediatek-volte-stack-firmware
2•todsacerdoti•11m ago•0 comments

Zero-Click Calendar Exfiltration Reveals MCP Security Risk in 11.ai

https://repello.ai/blog/zero-click-calendar-exfiltration-reveals-mcp-security-risk-in-11-ai
1•Dachande663•13m ago•0 comments

Claude Code is now a Bun single-file executable

https://twitter.com/jarredsumner/status/1943492457506697482
1•tosh•16m ago•0 comments

I built a Steam plugin for Dify (now listed on the official plugin marketplace)

https://marketplace.dify.ai/plugins/bdim/steam
1•bdim404•16m ago•1 comments

Apple vs the Law

https://formularsumo.co.uk/blog/2025/apple-vs-the-law/
2•tempodox•21m ago•0 comments

The Yellow Milkmaid Syndrome – paintings with identity problems

https://pro.europeana.eu/post/the-yellow-milkmaid-syndrome-paintings-with-identity-problems
1•thunderbong•22m ago•0 comments

Europe's first HPC ARM processor lands at TSMC

https://www.heise.de/en/news/Europe-s-first-HPC-ARM-processor-lands-at-TSMC-10483298.html
1•doener•24m ago•0 comments

Implement a robust multi-rate-limit throttling using Rails

https://www.prateekcodes.dev/implementing-api-throttling-multiple-endpoints-rails/
1•prateekkish•26m ago•0 comments

Foundation Models of Behavioral Data from Wearables Improve Health Predictions

https://arxiv.org/abs/2507.00191
1•tosh•29m ago•0 comments

Show HN: Gamified AI Tutor for School Students

https://www.edzy.ai/
1•gparashar•30m ago•0 comments

Are LLMs starting to become sentient?

https://garymarcus.substack.com/p/are-llms-starting-to-become-a-sentient
3•Duanemclemore•31m ago•1 comments

Gut microbes could protect us from toxic 'forever chemicals'

https://www.cam.ac.uk/research/news/gut-microbes-could-protect-us-from-toxic-forever-chemicals
1•timthorn•31m ago•0 comments

Mastering Postgres Replication Slots

https://www.morling.dev/blog/mastering-postgres-replication-slots/
1•gunnarmorling•32m ago•0 comments

Local Hole

https://en.wikipedia.org/wiki/Local_Hole
1•benbreen•33m ago•0 comments

GenAI Processors: Build powerful and flexible Gemini applications

https://developers.googleblog.com/en/genai-processors/
27•tzury•38m ago•0 comments

Show HN: Send RSS Feeds to Kobo E-reader

https://my.kobuddy.app/feeds
1•No-Arugula5818•38m ago•0 comments

Solar is EU’s biggest power source for the first time ever in June 2025

https://ember-energy.org/latest-updates/solar-is-eus-biggest-power-source-for-the-first-time-ever-in-june-2025/
4•pentacent_hq•39m ago•3 comments

OpenFront: Realtime Risk-like multiplayer game in the browser

https://openfront.io/
4•thombles•43m ago•0 comments

Ask HN: How to Create Data Flow Diagrams?

2•shivajikobardan•51m ago•0 comments

An almost catastrophic OpenZFS bug and the humans that made it

https://despairlabs.com/blog/posts/2025-07-10-an-openzfs-bug-and-the-humans-that-made-it/
4•r4um•1h ago•0 comments

Unusual USAF and Space Force Drills Near Taiwan

https://www.newsweek.com/us-air-force-department-level-exercise-indo-pacific-china-war-2097031
1•burnt-resistor•1h ago•1 comments

Ask HN: I built what I feel is a great API, but I'm stuck figuring out GTM

1•jdbohrman•1h ago•0 comments

GPS Accuracy Comparison

https://old.reddit.com/r/bicycling/comments/1lwqzcj/epic_gps_accuracy_comparison/
2•dkga•1h ago•1 comments

A Postgres Mystery: The SIGTERMs do nothing!

https://clickhouse.com/blog/sigterm-postgres-mystery
7•pradeepchhetri•1h ago•0 comments

MovieLens

https://en.wikipedia.org/wiki/MovieLens
1•leoh•1h ago•0 comments
Open in hackernews

An Enterprise-Level Retrieval-Augmented Generation System

https://comfyai.app/article/llm-applications/enterprise-level-rag-hands-on-practice-II
6•zljdanceholic•2mo ago

Comments

zljdanceholic•2mo ago
How can we search the wanted key information from 10,000+ pages of PDFs within 2.5 hours? For fact check, how do we implement it so that answers are backed by page-level references, minimizing hallucinations?

RAG-Challenge-2 is a great open-source project by Ilya Rice that ranked 1st at the Enterprise RAG Challenge, which has 4500+ lines of code for implementing a high-performing RAG system. It might seem overwhelming to newcomers who are just beginning to learn this technology. Therefore, to help you get started quickly—and to motivate myself to learn its ins and outs—I’ve created a complete tutorial on this.

We have a complete graph to explain its workflow where multiple tools are used: Docling for parsing PDFs, LangChain for chunking text, faiss for vectorization and similarity searching, and chatgpt for LLMs.

Besides, I also outline the codeflow, demonstrating the running logic involving multiple python files where starters can easily get lost. Different files are colored differently. The purpose of showing this is not letting you memorize all of these file relationships. It works better for you to check the source code yourself and use this as a reference if you find yourself lost in the code.

The original project of Ilya Rice design its RAG system for answering questions of annual reports from companies, so he only designed three types of question response format for that challenge: a name, a number, or a boolean. But to ask questions about technical stuff, we absolutely ask general questions like How does RoPE work? to know about some concepts and the like Therefore, I further modify the system logic to fit this need by customizing an AnswerWithRAGContextExplanationPrompt class and automatically matching the most related chapter and corresponding pages via searching through all faiss databases (only retrieve the top-1) The final performance is demonstrated below (not cherry-picked, only tested once).

- What's the steps to train a nanoGPT from scratch? The answer is:

Training nanoGPT from scratch involves several clearly defined steps. First, set up the environment by installing necessary libraries, using either Anaconda or Google Colab, and then download the dataset (e.g., tinyShakespeare). Next, tokenize the text into numerical representations and split the data into training and validation sets. Define the model architecture including token/positional embeddings, transformer blocks with multi-head self-attention and feed-forward networks, and layer normalization. Configure training hyperparameters and set up an optimizer (such as AdamW). Proceed with a training loop that performs forward passes, computes loss, backpropagates, and updates parameters, while periodically evaluating performance on both training and validation data. Finally, use the trained model to generate new text from a given context.

All code are provided on Colab and the tutorial is referenced here. Hope this helps!