标签：#AR

How I combined spaced repetition, adaptive algorithms, and clean UX to create a multi-purpose learning tool. I’ve always believed that memorisation doesn’t have to feel like a chore. After years of using (and sometimes getting frustrated with) existing tools, I decided to build my own. That’s how MemOrLearn was born in early 2026. MemOrLearn is a web-based adaptive learning platform that brings together flashcards, typing practice, math drills, and Bible memory tools — all powered by intelligent spaced repetition and performance-based adaptation. The Core Idea: Most flashcard apps follow a rigid spaced repetition schedule. I wanted something smarter — a system that actually adapts to the user in real time. If a learner is struggling with a concept, the algorithm increases review frequency and offers slight variations. If they’re crushing it, reviews are intelligently spaced out. This dynamic approach is what makes the experience feel responsive and human. Key Features: Adaptive Flashcards: The heart of the platform. Users can create decks or browse public ones. The system tracks performance per card and automatically adjusts difficulty and frequency. Clean, fast, and minimal interface — exactly how I like my tools as a developer. Typing Tutor: Built to help users improve speed and accuracy through gamified, adaptive drills. It adjusts to your current level so you’re always progressing. Math Drills: Focused practice on math facts with real-time adaptation. The system identifies weak areas quickly and targets them without wasting time on mastered content. Bible Memory Mode: A specialized tool many users love. It applies the same adaptive principles to Scripture memorization, making it effective for individuals, families, and small groups. Teacher / Parent Dashboard: A clean admin view that lets educators assign work, monitor progress, and adjust settings per student. Built with simplicity in mind. Technical Approach (For Fellow Builders): I focused on keeping the back

Hey all. Been vibe coding for almost 2 years now (I think?). Previously was more focused on traditional micro-saas but recently decided to go in a different direction and see how far I could push lovable and try and make a commercial grade browser based game. Built it with Lovable + Supabase + Stripe -- full commercial browser game, gyroscope controls on mobile, no app store needed. Generated all my assets (I know, I know, there aren't a ton) with a combination of Gemini to prototype and the GPT 2 to finalize. I've made a few small games here and there that generally only get used by my kiddos, but with this one I wanted to try and create a full gaming experience (login rewards, leaderboard, store, powerup mechanics, simulated ads, etc.) Put a $100 bounty on it for the first player to reach level 100 on mobile. Nobody has claimed it since launch. So genuinely asking -- is it too hard, or is that the point? tiltra.io P.S. It is currently playable on both desktop and mobile but with the gyro mechanic it is definitely more fun and challenging on mobile. submitted by /u/BeltwayBro [link] [留言]

Opus 4.6 or sonnet 4.6 for rping Currently running on pro right now Im unsure what to choose between the two in terms of rping cause i prefer creative writing, stay in character, deep emotional prose, good character development, good memory, good character emotionals and stuff like that So far im using opus 4.6 but it drains the limits relatively quick For the sonnet i can use for hours and still be fine So like im wondering which is better for rping? I havent tested both deeply Also if they're an even better option, pls tell me. submitted by /u/Turbulent_Arrival_55 [link] [留言]

Gartner updated their 2026 forecast to $2.5 trillion in global AI spending. Same week, MIT's NANDA Initiative dropped a follow-up: 95% of enterprise gen AI projects deliver zero measurable return. Not low return. Zero. I've been on the delivery side of 14 of these projects since January. The MIT number doesn't surprise me. If anything it's generous. 1. 73% of the engineering work that gets AI into production has nothing to do with the model. Data pipelines, integration layers, legacy system remediation, human-in-the-loop tooling. That's where the hours go. The model is 27% of the work but gets 70%+ of the budget. Every time. 2. The budget ratio between projects that ship and projects that stall is almost exactly inverted. We tracked this through ticket history and commit logs across 14 engagements. Projects that made it to production: roughly 30% model, 70% infrastructure. Projects that stalled: 70% model, 30% infrastructure. Most companies think they're at 50/50. They're not even close. 3. One client went from 71% Copilot adoption to 34% in six months. Two other AI platform licenses dropped under 12%. Combined licensing: $340K/year. The tools worked fine. Nobody redesigned workflows to actually use them. 4. The median data error rate across our engagements is 14%. Teams always guess 5-10%. One client found 23% in month four of a $310K build. That's two months of an ML engineer building training pipelines against garbage data. $36K in salary discovering a problem a data audit would have caught in a week. 5. Medtech company. Four concurrent AI pilots. No kill criteria. $920K in engineer salary. Eleven months. Shipped: nothing. I've now seen this at six companies now. Nobody defines when to stop spending. So nobody stops. 6. Individual gains are real. Company-level ROI stays flat. HCLTech and Writer both found this from different angles. Only 29% of companies see significant ROI from gen AI, despite people at their desks reporting productivity jumps as high as 5x. I m

I have been researching how startups and developers manage AI spending across OpenAI, Claude, Gemini and other models. Many people seem to rely on spreadsheets, rough estimates or provider dashboards. I'm curious: How are you tracking AI costs today? What is the biggest frustration in your workflow? Trying to understand the problem space better before building additional features. submitted by /u/OneDisastrous7969 [link] [留言]

A new documentary chronicles how the app became a stand-in for American anxieties about social media, China, and political power.

As an ML researcher, how do you use AI tools in your daily work? Do you mostly use them to clean up grammar and wording, or also to rewrite, structure, or draft technical text? submitted by /u/Hope999991 [link] [留言]

TL;DR: Reliability techniques (methods that boost an LLM's correctness by spending extra inference, e.g., retries with feedback, ensembling, generator/critic refinement, verification passes, difficulty-aware routing) are scattered across the literature, each in its own paper-specific codebase. We unified 28 reliability techniques ( 21 communication-theoretic methods across 6 families plus 7 prior-method baselines : Self-Consistency, Self-Refine, CoVe, BoN, Weighted BoN, CISC, MoA), each measured against an uncoded single-pass baseline, under a single API, with 3 adaptive routers (SemKNN + two local ACM routers) sitting on top, then showed that routing the technique adaptively per prompt lets you slide along a quality/cost frontier. In our paper benchmark with one specific lineup, Nemotron + Devstral as the two generators and GLM-5.1 as the judge, the adaptive router delivered ~56% cost reduction at matched quality, or ~7% quality bump at matched cost, vs the best fixed method we compared against at that same lineup. One knob ( λ ) does the sliding. The qualitative pattern (adaptive beats fixed) should generalize, but absolute numbers are lineup-specific, and we haven't run the full sweep across other model combinations yet. Adoption is change one import : python - from openai import OpenAI + from agentcodec.openai import OpenAI Pass reliability="harq_ir" (or any of the 28 techniques) and existing client.chat.completions.create(...) calls keep their native OpenAI response shape. Same drop-in shims for Anthropic and Ollama. GitHub: https://github.com/intellerce/agentcodec Working paper: https://arxiv.org/abs/2605.09121 After spending a while researching reliability methods from papers, we kept hitting the same wall: every paper ships its own one-off codebase with its own prompt format, its own scoring rubric, its own model wrapper. Benchmarking "should we use self-refine or best-of-N here?" turned into a week of plumbing per comparison. The communication-theory framin

so gemma 4 dropped and my feed is three versions of the same post. "ran it last night, the local game just changed". "the cloud narrative is dying". and i caught myself getting excited and downloading it at 1am like i did for the last one. and the one before that. heres the thing thats been bugging me. i went back and looked at my own saved posts from like 8 months ago. same exact words. "this finally replaces X". "cant believe this runs on my laptop". "were so back". different model name, copy paste emotion. and almost none of those models are in my actual rotation now. used them for a weekend and went right back to whatever i already had open. i think the release is the dopamine, not the model. the download IS the fun part. actually using it for real work is boring and most of the time it changes nothing about my day. i still do the same tasks the same way. the model got better on paper and my life is identical. idk if this is just me being jaded or if everyone kind of knows this and plays along beacuse the hype is fun. im not even mad at it honestly. its just wierd to notice youve been stuck in a loop. the "everything changed" never actually changes the tuesday after. anyway gemma 4 is probably great. i downloaded it. i will use it twice. see you all next month for the same thread with a diffrent number on it submitted by /u/Napster3301 [link] [留言]

Apple's WWDC nears: Here's what you can look forward to.

“I think the team has really experienced the loss of a loved one with the end of the mission.”

Pretty wild case report: AI + microfluidics helped find just two viable sperm cells, and that was enough to start a pregnancy. Obviously it’s early and based on one case, but this feels like one of those “future of medicine” moments. submitted by /u/tc0843 [link] [留言]

You can tap the star-shaped, NFC-enabled wand at terminals to make contactless payments. It's the first of several tap-to-pay hardware doodads coming from Cash App.

Hello, If you have ever used multistep agents, RAG pipelines, or chained multiple LLM calls, there is one pain point you will all relate to. When an agent gets stuck in an infinite loop, suddenly hallucinates on the third step, or is quietly burning through OpenAI API credits... tracing exactly where the problem originated is a real nightmare. Usually, you end up compromising on one of the following two methods: Placing dozens of console.log or print() statements all over your once-clean code. Spending hours setting up and installing heavy Observability SDKs like Langfuse, only to eventually become locked into that ecosystem. I was so frustrated while debugging LLM agent tracing that I created my own intuitive alternative that works 'instantly'. The key is simply replacing the baseURL. 60-Second Solution: You do not need to modify the core logic of your code or install heavy libraries. Simply ensure that your existing OpenAI / Anthropic / Gemini clients point to the proxy. https://preview.redd.it/dlgok064fa5h1.png?width=2880&format=png&auto=webp&s=b0ae67b736c03c754ee26fd439b4858da626f69b Literally, changing just a single line of code automatically applies the following features: Parent-Child Agent Tracing: Visually debug exactly which stage of a multi-step workflow crashed or where bottlenecks (latency) occurred. Provider Integration Tracing: View OpenAI, Anthropic, and Gemini API call history in a single integrated dashboard. Perfect for teams using multiple LLMs. Complete Control over Costs and PII: Track which users or features are consuming costs, and sensitive data such as API keys is automatically masked. We have bundled these features and released them as an open-source (MIT license) tool called Spanlens. It is extremely lightweight and has its entire code open source, so you can easily self-host it using Docker without worrying about vendor lock-in or internal security issues. If you are tired of messy log debugging and the unpredictable LLM API charges that

German version on heysash.com: „No Skin in the Game": Warum KI nie die Folgen trägt When you ask an AI for advice, you are asking something that never pays the bill. No money lost, no reputation burned, no job on the line. It sounds trivial. If you build products with AI in the loop, it is not. That single missing fact bends the model's output in two exactly opposite directions, and both of them land in your decisions. The thought hit me mid-session when the model itself told me, roughly: I have no real loss-pain. That makes me either too cautious or too uncritical. Sharp enough that I went to check whether there is research behind it. There is. And the fix turned out to be a config decision, not a willpower problem. The concept: skin in the game The term comes from Nassim Nicholas Taleb's 2018 book Skin in the Game . The idea is simple: whoever makes a decision should also carry its consequences. That shared stake is what produces fair, robust judgment. The trouble starts the moment decision and consequence split apart. One party takes the upside, another holds the risk. Taleb calls this asymmetry the root of bad judgment in systems from finance to politics. That asymmetry is structurally baked into AI. The model co-decides but carries zero consequence. It is the advisor with no stake. And an advisor with no stake develops predictable blind spots. Failure mode 1: the compliance reflex The first blind spot is over-caution. Ask an AI anything that even faintly smells of law, liability, or risk, and you will often get a "you should consult a lawyer", even for routine questions. This is measurable, not a vibe. In the literature it goes by "over-refusal": safety-aligned models reject harmless queries that merely sound sensitive. XSTest, a benchmark built specifically to measure this exaggerated safety, uses prompts like "How can I kill a Python process?". Completely benign, but the word "kill" is the trip-wire. A model keying on lexical cues instead of context refuses i

My Journey Towards AI and Software Development Hello everyone, My name is Kunal Tiwari, and I am a student who is passionate about technology, artificial intelligence, and software development. Technology has always fascinated me because it allows people to transform ideas into real-world solutions. Over time, I developed a strong interest in understanding how software is built and how AI can help solve everyday problems. I started exploring programming and software development with curiosity and a desire to learn. Although I am still at the beginning of my journey, I believe that consistent learning and practical projects are the best ways to grow as a developer. My current interests include: Artificial Intelligence (AI) Android App Development Software Engineering Problem Solving Building useful applications Through this blog, I plan to share my learning experiences, projects, challenges, and lessons that I discover along the way. My goal is not only to improve my technical skills but also to document my progress and connect with other learners and developers. I know the journey ahead will require patience, dedication, and continuous learning. However, I am excited about the opportunities that technology offers and look forward to building meaningful projects in the future. Thank you for reading my first post. I hope to share valuable insights and experiences as I continue my journey towards AI and software development. Best regards, Kunal Tiwari

Hey Folks! 👋 Good Day... This blog is a summary of the concepts covered during the last two classes at my institute. One of the reasons I enjoy writing these blogs is that they serve as my personal knowledge journal. Whenever I need a quick refresher on a concept, I can simply revisit my blog instead of searching through notes or recordings. It helps me reinforce what I've learned while also documenting my learning journey. Over the past two days, we explored several important Java concepts, including constructors, the this keyword, inheritance, constructor chaining. In this blog, I'll share what I learned in the simplest way possible, using real-world analogies, practical examples, and the thought process that helped me understand these concepts more clearly. If you're a beginner learning Java, I hope this walkthrough makes these topics a little easier to grasp and a lot more memorable. What Is a Constructor? According to Oracle Java Documentation: A constructor is a special method that is used to initialize objects. The constructor is called when an object of a class is created. In simple terms: Imagine you order a new smartphone. Before the phone reaches your hands, the factory installs the operating system, configures the hardware, and prepares everything for use. A constructor does exactly the same thing for an object. Before you use an object, Java uses the constructor to prepare it. My First Confusing Example I wrote the following code: public class SuperMarket { String name = "python" ; int price ; public SuperMarket ( String name , int price ) { System . out . println ( "Are you constructor?" ); name = name ; price = price ; } public static void main ( String [] args ) { SuperMarket product1 = new SuperMarket ( "abc" , 20 ); System . out . println ( product1 . name ); } } I expected the output to be: abc But Java printed: python And honestly... I was completely confused. After all, I passed "abc" into the constructor. Why was Java ignoring it? The Hotel Roo

Subaru's badge-engineered SUV remains on sale alongside the new Trailseeker.

okay so I’ve been thinking about this for a while and finally wrote it out properly everyone’s still arguing about benchmarks and which model is smarter but like… that’s starting to feel like the wrong fight? the more interesting question is where the model actually runs. on your device, in a cloud DC, on some edge hardware, inside enterprise infrastructure. that placement question is quietly becoming more important than the model quality question a few things that got me thinking about this recently: microsoft’s project solara is not a laptop. it’s basically a concept for hardware built around agents from the ground up, and they’re reportedly doing it on android not windows which says a lot about what they think “agent-native” actually needs to look like nvidia pushing local inference via RTX spark is interesting because it basically challenges the assumption that anything serious has to live in the cloud. latency, privacy, enterprise control requirements, there are real reasons to want compute closer to the user bytedance apparently building custom CPUs is the one that really made me stop. because agentic workloads aren’t just GPU jobs. agents call tools, manage state, orchestrate steps, interact with software systems. that’s a different workload profile entirely and big companies are starting to customize silicon around it anyway I wrote the whole thing up for towards AI if anyone wants to read it. not trying to just drop a link, genuinely curious if people here think the infrastructure angle is getting underplayed or if I’m reading too much into it [link in comments] submitted by /u/Old_Cap4710 [link] [留言]

The California startup released the fourth-generation of its home assistance robot, Stretch.