Fiber and cable internet generally provide the fastest speeds, while 5G and fixed wireless are growing as flexible options. Fiber is ideal for heavy users like gamers and remote workers, while cable ...
I recently tried building a small agent for coaching centers.The idea was simple: a teacher uploads a syllabus or notes, and the agent generates a test paper from that material. The hard requirement was reliability. No invented questions, no drifting outside the syllabus.Instead of trying to “fix” hallucinations with better prompts, I constrained the agent’s job very narrowly.I defined:a fixed knowledge base (only the uploaded syllabus)explicit tools the agent was allowed to usea structured outp
I ran a systematic comparison of AI content detection and humanization tools after a client terminated a contract over an AI detection flag (87% AI-generated on content I'd manually edited).*Methodology:* - 31 tools tested over 90 days - 200+ content samples (technical docs, marketing copy, blog posts, academic-style) - Measured detection accuracy against known AI/human content - Measured humanization "bypass rate" against Originality.ai (industry standard) - Controlled for c
Tenmo A lightweight tensor library and neural network framework written in pure Mojo.https://github.com/ratulb/tenmoTenmo focuses on:SIMD-optimization explicit memory layout zero-copy views a minimal but practical autograd system Status: Tenmo evolves alongside Mojo itself. APIs may change. Not production-ready yet.Performance MNIST (4-layer MLP, 105K params, 15 epochs) Platform Device Avg Epoch Total Test Acc Tenmo CPU (Mojo) 11.4s 171s 97.44% PyTorch CPU 14.5s 218s 98.26%
I'm working on a project that needs to dynamically generate simple icons and diagrams. I've tried GPT-4 and Claude - they can output SVG code but the results are hit or miss, especially for anything beyond basic shapes.<p>Has anyone found a reliable workflow for this? I'm wondering if there are specialized models, better prompting techniques, or if I should just use a traditional graphics library and skip the LLM route entirely. What's actually working in production for you?
Hi HN — completed a PhD and exploring a startup or lab-to-product path around next‑generation memory technologies and neuromorphic devices (compute‑in‑memory, memristive devices, analog/mixed-signal accelerators), with a strong emphasis on theory-driven materials discovery and device physics (modeling → candidate materials → prototype path).Is anyone here looking for a cofounder in this space, or interested in exploring one of these directions together?Technical cofounder: device physics, m
We ran a 500-cycle benchmark to test long-horizon coherence, reasoning stability, and identity persistence in large language models.The experiment used the Sigma Runtime, a model-agnostic control layer that adds long-term memory, structural coherence tracking, and adaptive equilibrium regulation to standard LLMs. It enables stable reasoning and personality continuity across hundreds of interactions without context resets.Protocol overview - 500 reasoning cycles divided into 10 blocks of 50 quest
I spent the last few months frustrated with RAG hallucinations and the cost of vector databases ($2k/mo for Pinecone).I realized that Cosine Similarity is often a weak proxy for semantic truth, so I built a memory protocol based on Wasserstein-2 Distance (Optimal Transport) instead.The core idea: Treat memory as a geometry problem. By measuring the transport cost between the "Stored State" and the "Retrieved State," we can mathematically enforce coherence. If the transpo
I ran a systematic comparison of AI content detection and humanization tools after a client terminated a contract over an AI detection flag (87% AI-generated on content I'd manually edited).*Methodology:* - 31 tools tested over 90 days - 200+ content samples (technical docs, marketing copy, blog posts, academic-style) - Measured detection accuracy against known AI/human content - Measured humanization "bypass rate" against Originality.ai (industry standard) - Controlled for c
The goal is to make you pick one:1. Am I deeply delusional? 2. Am I hyper aware?Please pick one before you remove this post.There is no self running feedback loop that's why they hallucinate. THESE LLMs only know one direction and that is feed-forward. THINKING does not work this way and prompts are not how humans function. Emulating thinking also does works till only a certain limit, the original network does not know where the right answer is or where to stop thinking. these models do not
We’ve been working on a reusable insulin patch pump for the past year, and I wanted to share some of the engineering lessons learned so far. Insulin micro-dosing is mechanically non-trivial: basal delivery can be fractions of a unit per hour, boluses involve larger step changes, and everything has to stay within ISO tolerances while attached to a moving human body for several days.Some interesting constraints we ran into:- dose accuracy limits from ISO 11608 (both basal and bolus) - fluidic comp
World Cup Trump GIF
Five Nights At Freddy GIF
Good Morning Ai GIF
Happy Fun GIF by Zuny
Modem Falling GIF by Telletec
I keep seeing two extreme futures discussed around AI.One is techno utopia: AI does everything, productivity explodes, humans are free to create and chill.The other is collapse: AI replaces jobs, wealth concentrates, consumption dies, society implodes.What I don’t see discussed enough is the mechanism between those states.If AI systems genuinely outperform humans at most economically valuable tasks, wages are no longer the primary distribution mechanism. But capitalism today assumes wages are ho
We’ve been working with auditors and compliance teams in regulated sectors (finance, healthcare) and hit a wall: LLM risk assessments are subjective, non-reproducible, and don't satisfy growing regulatory demands for evidence, not just opinions.So we built EB3F (Evidence-Based Framework for Foundational Models). It's a packaged methodology that transforms an audit from a consultant's report into a standardized process for producing reproducible, legally-admissible exhibits.The twi
Most "Voice AI" demos today are just API wrappers around cloud LLMs. They are impressive, but they fail in production for three reasons: Latency, Compliance, and Lack of Control.I built VAAK (Voice-Activated Autonomous-Knowledge-System) to solve exactly this.It’s not just a "local chatbot." It is a domain-agnostic, production-grade Conversational Operating System built entirely in Rust.Here is why Senior Architects and Product Leaders should pay attention:1. The "Determi
Hi everyone, I'm Vivien one of the creators of Bound.I'll keep things brief, there are two things I hate about using AI coding models: paying a subscription and how bad they are for the environment.So my cofounder and I built Bound. Simply put, we provide Cursor's "tab" feature but instead of using a cloud model we use an ultra-small local LLM trained on your own repo (that and a couple other tricks to make sure it's the same quality as cursor). The nice thing here