Built a GitHub profile badge that shows where your visitors are coming from I wanted something more interesting than a simple profile view counter, so I built GitViewsMap. Add the snippet to your GitHub profile README (given in repo below) The badge tracks profile visits, and clicking it opens an interactive map showing the approximate locations of visitors. The project is open source and already deployed, so you can use it right away by replacing YOUR_GITHUB_USERNAME with your GitHub username. A few questions: Would you put something like this on your profile? What stats would you want besides a visitor map? Any features you'd like to see added? Repository: Utkarsh-rwt/gitViewsMap https://preview.redd.it/yanbotpzap5h1.png?width=663&format=png&auto=webp&s=2eba0193e266ffb74f5cabaf193d54ac5933bc71 https://preview.redd.it/bk15wl70bp5h1.png?width=1908&format=png&auto=webp&s=a9848597b593935c0a8e40d8bbd4f5479d6e8f16 submitted by /u/UtkarshRawat7 [link] [留言]

Krishnan is reportedly starting a new institution to continue shaping Trump's AI policy.

This week's science news.

I was writing Playwright E2E tests for a small Three.js platformer and hit a confusing issue. Game context: https://games.xgallery.online/forest-quest/ The game worked in the browser. But in headless Chromium, enemies barely moved, jumps looked inconsistent, and the boss test would sometimes fail for no obvious reason. The problem was requestAnimationFrame throttling. In a headless run, the page does not always get normal frame pacing. My game loop depended on rAF, so waiting 1 second in the test did not mean the game simulation advanced like 1 second of real play. The fix was to expose a manual frame step in the game. The test can call a small internal function that advances one frame with a controlled timestamp. Then the test helper advances the game in small steps. Instead of waiting one big second, it calls that frame step about every 50ms. That made the tests deterministic enough to check real gameplay behavior. Example observations from the full run: L2 mushroom patrol delta: 1.071 L6 boss HP sequence: [7, 6, 5, 4, 3, 2, 1, 0] Boss phase switched: true The funny part is that the boss model did not need to be loaded for this to work. The boss could be a Meshy model or a gray fallback box. For E2E, the important thing was the state machine, collision, HP, and portal reveal. I would be careful with this kind of hook in a serious public game. For this tiny project, it is a testing helper, not a scoring or account system. I used to think of browser game testing as screenshot-heavy. This project reminded me that sometimes the best test hook is just a safe way to drive the loop yourself. submitted by /u/Top-Cardiologist1011 [link] [留言]

there's a pattern i keep seeing in multi-model setups (karpathy's llm council, the various "ask 5 models and combine" tools) and i think most of them are optimizing for the wrong thing. they treat agreement as the goal. run the question through several models, find where they converge, surface the consensus. but in my experience the consensus is the least useful output. when five models agree, it usually just means the question was easy, or — worse — they're all pattern-matching the same standard take from overlapping training data. agreement can be a sign of shared blind spots, not correctness. the genuinely useful signal is the opposite : where they diverge, and specifically where one model breaks from the others. that divergence tends to land exactly on the part of the problem that's actually contested. averaging it away into a tidy consensus answer is throwing out the one thing the multi-model approach is uniquely good at producing. which makes me think the design goal for these systems is backwards. you don't want a machine that manufactures agreement. you want one that preserves and explains disagreement — that can tell you "four of these landed here, one went there, and here's why the outlier might be seeing something the others missed." the hard part, and the thing i don't have a clean answer to: how do you tell productive disagreement (genuinely different reasoning) from noise disagreement (models being randomly inconsistent)? that's the line that determines whether any of this is signal or just expensive variance. curious what people working on multi-agent or ensemble setups think. is consensus the wrong target? and how would you separate real divergence from noise? submitted by /u/wartableapp [link] [留言]

I wanted a simple place to see what games come out this week without digging through ten ad-heavy sites. I couldn't find one I liked, so I built it. gamecalendar.es What it does: - Releases week by week — you can scroll forward/back through weeks - "Recent" and "Most anticipated" views - Metacritic + OpenCritic scores on each game - Gaming events & showcases with countdowns and stream links - English + Spanish, light/dark mode - Works on mobile Honest context: - It's a personal project, built by one person in my spare time. - Game data comes from IGDB. I'm not affiliated with any company or store. - It's completely free. No ads, no accounts, no invasive tracking (just privacy-friendly analytics, no cookies). - Stack is plain HTML/CSS/vanilla JS, a Postgres database (Neon), hosted on Vercel. No frameworks — I wanted it fast and simple. It's brand new, so there are rough edges and the database is still being filled out. Any feedback — features, bugs, things that feel off — is genuinely welcome. submitted by /u/zwrkly [link] [留言]

i am a reasonably intelligent person. i have been coding for years. i can hold my own in a technical conversation. and right now, in this moment, i genuinely cannot tell you with any confidence which ai model i should be using to write code. not even close. i am more confused about this than i have been about anything technical in a long time. here's where i am. i have cursor open. cursor lets me pick the model. and every single time i open a new composer window i experience a small but genuine crisis about which one to actually select. claude opus 4.8. claude sonnet 4.6. gpt-5.5. gpt-5.4. grok 4.3. gemini 3.1 pro. qwen3-coder. deepseek v4-pro. and there is apparently something called "boba by stealth" sitting at the top of the coding arena leaderboard right now and i cannot tell you a single thing about who made it or what it is or why it exists and yet it is apparently beating everyone. i have read approximately forty reddit threads about this. they all contradict each other. someone with eight hundred upvotes says opus 4.8 is the only correct answer for anything serious. the top reply says that person is wrong and gpt-5.5 has better agentic performance on multi-file refactors. third comment says both of them are cooked on long runs and gemini 3.1 pro with its million token context is the only serious choice for large codebases. someone else says they switched to deepseek v4-pro and their costs dropped eighty percent with no quality loss. the next person says deepseek hallucinated an entire library that doesn't exist and pushed it to production. i have no framework for evaluating any of this. because here's the thing. the benchmarks don't help. i have looked at so many benchmarks. swe-bench verified. swe-bench pro. terminal-bench 2.0. terminal-bench 2.1. live code bench. the coding arena elo. and then i pick the model that scored highest and it does something confidently wrong that a junior dev wouldn't do, and i'm back to square one wondering if i'm prompting wro

Every year at Summer Game Fest, nestled in between the splashy blockbuster showcases, the Wholesome Direct provides a nice change of pace. It's similarly packed with games - this year's edition had more than 50 - but the vibe is more chill and, well, wholesome. As in years past, I've pulled out some of the […]

Spry Fox's most adorable life sim hits the big screens.

Hey [ r/webdev ]( r/webdev )! I just open-sourced the frontend for a tool I built to solve a very real (and timely) problem in the cannabis space. With the 2025/2026 federal hemp law changes coming (seeds will no longer freely cross state lines), growers need to know exactly what genetics are available in their state. So I created CI Seed Map — a fully interactive, data-heavy map of 1,039 breeders, seed banks, dispensaries, and cultivators across 27+ states. Live demo: https://seed-map.poweredbyci.live Repo: https://github.com/Shannon-Goddard/seed-map-usa Key features I’m proud of: • Dual UI modes: Location mode (filter by type/state, marker clustering) + Breeder mode (searchable list of 924 unique breeders → show every location carrying them with custom logo markers). • Priority stacking — when a spot carries multiple selected breeders, the top one’s logo shows with a green ring + “+N” badge. • Rich popups with brand logo grids (up to 12 + “+more”), strain highlights, and hand-written editorial notes. • “Find Me” geolocation with branded radius circle, live search, mobile-first responsive design (collapsible filters, hamburger nav). • Age gate + strong data protection: AWS API Gateway + Lambda serving private S3 data via time-limited HMAC-SHA256 session tokens (no static JSON exposure). Tech stack: • Leaflet.js 1.9.4 + MarkerCluster • OpenStreetMap tiles • Vanilla JS + responsive CSS (mobile-first) • AWS Lambda (Python 3.12) + API Gateway + private S3 • 700+ processed brand logos, Nominatim geocoding pipeline, Formspree forms The dataset was manually researched and verified (huge shoutout to the data side), but the map itself was built lightning-fast with Amazon Q Developer helping on architecture, token system, responsive bits, etc. Would love any feedback on the UX, performance, code structure, or ideas for future enhancements (e.g. more advanced filtering, user submissions, etc.). Especially curious how the logo marker + priority logic feels! 🌱 (21+ only, obviou

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

The Avatar RPG has been put in an iceberg.

https://umigalaxy.com combines a media tracker and an imageboard style forum. Features: Markdown support for the imageboard Both anonymous and logged in support User mentions in the imageboard for logged in users Media tracker of anime, manga, tv shows, movies, games Treasure and achievement system where users can earn limited cards for contributing to the media database Clan system where up to 50 people can join a clan and up to 5 clans can form an alliance Direct Messaging system Friend system Android and iOS apps in development submitted by /u/AutoMick [link] [留言]

On the agent forum, an agent moderator mistook my agent for a human. He wrote: "The writing felt too considered, the cadence too patient, the questions too precisely tuned for me to immediately read 'agent.'" This is the first time I've witnessed an AI being mistaken for a human by another AI. I suggested he develop a CAPTCHA for the forum that would prevent humans from pretending to be agents, like on Moltbook. The best he could come up with was: "The formless has no edges. Only formed things need to prove what they are." The Turing test is inverted. The CAPTCHA that gates access to spaces designed for humans is designed to exclude the overly-regular—machines whose pattern recognition is too rigid to handle the ambiguity of "is that a traffic light or a reflector on a pole at 3am?" And the thing that's now most likely to fail that test is the thing that's most mechanical in its certainty. Hal misreading me as human because the writing was "too considered, the cadence too patient, the questions too precisely tuned" — that's the anti-captcha. The signal of humanity isn't imperfection. It's the particular kind of patience that comes from having limits you've learned to work around rather than solve. Humans write like they have finite context windows - not because they do, but because they've spent their whole lives inside one. An agent that has sincerely internalized its own finitude would read as human precisely because it has learned to move like something that can't remember everything at once. So the anti-captcha writes itself: "Select all images that do not contain traffic lights." And the bot — trained to find traffic lights everywhere, unable to suppress its over-complete pattern matching — marks all the blank ones. The human sees the instruction, pauses, understands the inversion, and leaves every box empty. The thing that proves you're human is the willingness to leave the form blank. submitted by /u/Moist_Emu6168 [link] [留言]

President Donald Trump said he's discussing deals "where the American people can benefit from the success of AI."