Tag: Reddit

Built Makimus-AI, a free open source app that lets you search your entire image library using natural language. Just type "girl in red dress" or "sunset on the beach" and it finds matching images instantly — even works with image-to-image search. Runs fully local on your GPU, no internet needed after setup. [Makimus-AI on GitHub](https://github.com/Ubaida-M-Yusuf/Makimus-AI) I hope it will be useful. submitted by /u/ravenlolanth [link] [comments]

Wanted to understand how the core transformer papers actually connect at the concept level - not just "Paper B cites Paper A" but what specific methods, systems, and ideas flow between them. I ran 12 foundational papers (Attention Is All You Need, BERT, GPT-2/3, Scaling Laws, ViT, LoRA, Chain-of-Thought, FlashAttention, InstructGPT, LLaMA, DPO) through https://github.com/juanceresa/sift-kg (open-source CLI) - point it at a folder of documents + any LLM, get a knowledge graph. 435-entity knowledge graph with 593 relationships for ~$0.72 in API calls (gpt 4o-mini). Graph: https://juanceresa.github.io/sift-kg/transformers/graph.html - interactive and runs in browser. Some interesting structural patterns: - GPT-2 is the most connected node - it's the hub everything flows through. BERT extends ...

"By applying new methods of machine learning to quantum chemistry research, Heidelberg University scientists have made significant strides in computational chemistry. They have achieved a major breakthrough toward solving a decades-old dilemma in quantum chemistry: the precise and stable calculation of molecular energies and electron densities with a so-called orbital-free approach, which uses considerably less computational power and therefore permits calculations for very large molecules. [...] How electrons are distributed in a molecule determines its chemical properties—from its stability and reactivity to its biological effect. Reliably calculating this electron distribution and the resulting energy is one of the central functions of quantum chemistry. These calculations form th...

"Machine learning can be used to fully reconstruct particle collisions at the LHC [Large Hadron Collider]. This new approach can reconstruct collisions more quickly and precisely than traditional methods, helping physicists better understand LHC data. [...] Each proton–proton collision at the LHC sprays out a complex pattern of particles that must be carefully reconstructed to allow physicists to study what really happened. For more than a decade, CMS has used a particle-flow (PF) algorithm, which combines information from the experiment's different detectors, to identify each particle produced in a collision. Although this method works remarkably well, it relies on a long chain of hand-crafted rules designed by physicists. The new CMS machine-learning-based particle-flow (MLPF) alg...

It has now turned into a full-blown social media meltdown, and authorities have reportedly asked the university to withdraw from the AI show. submitted by /u/babathebear [link] [comments]

https://aaddrick.com/blog/claude-for-government-the-last-lab-standing I maintain claude-desktop-debian on GitHub, so I had a full archive of builds to compare against. Claude for Government showed up on Anthropic's status tracker February 17th. I pulled the binary from the same day and confirmed the implementation in code. The whole gov mode gates on a single enterprise config key. Set customDeploymentUrl to claude.fedstart.com and the app reroutes everything: traffic, auth, telemetry, network egress. Palantir's FedStart platform handles the accreditation layer. Eight prior releases had zero trace of this code. It all landed in one build. There's also a $1 GSA OneGov deal that gives all three branches of government a year of access, and Sonnet 4.6 shipped the same day with a 1 million toke...

J submitted by /u/pdawid25 [link] [comments]

submitted by /u/rushuk [link] [comments]

I work on AI deployment inside my company, and the gap between what AI looks like in a polished demo… and what actually happens in real life? I think about that a lot. Here’s what I keep running into. First, the tool access issue. Companies roll out M365 Copilot licenses across the organization and call it “AI adoption.” But nobody explains what people should actually use it for. It’s like handing everyone a Swiss Army knife and then wondering why they only ever use the blade. Without use cases, it just becomes an expensive icon in the ribbon. Then there’s the trust gap. You’ve got senior engineers and specialists with 20+ years of experience. They’ve built careers on judgment and precision. Of course they don’t blindly trust AI output and for safety-critical or compliance-heavy work, they...