Neptune

Neptune AI

Neptune AI

Neptune AI

Neptune AI

Neptune AI

Neptune AI

Neptune AI

Neptune AI

Neptune AI

Neptune AI

I built a real-time space simulation with fable 5

visit orrery.xaney.dev to check it out. I recently wanted to test out fable 5 so I tried building a realistic space simulation with its own physics engine, and I was very surprised by the single shot result, it uses a real physics engine for calculations, and it includes: - all the planets in out solar system including all the moons and dwarf planets - Milky Way galaxy - Sagittarius black hole - ~4k stars and their planetary systems that you can visit in the Milky Way all with realistic textures, real physics and real-time locations it still contains some bugs that I havent fixed but I was too excited about it so I wanted to showcase this note: everything in this project was made by Claude Fable 5 in 3 prompts, I didnt do or change anything in the code https://preview.redd.it/tbapx7mvrz6h1.png?width=3072&format=png&auto=webp&s=19182fbdc261a681b6d0ab09b26280612e775baf https://preview.redd.it/4jt9e7mvrz6h1.png?width=3072&format=png&auto=webp&s=8bb08c81fbd502ce7996c628c6b028d014cb22ca https://preview.redd.it/s8pph8mvrz6h1.png?width=3072&format=png&auto=webp&s=37cea7e8e9ffbd366146dffa3013dff486ae6a82 https://preview.redd.it/0f57n8mvrz6h1.png?width=3072&format=png&auto=webp&s=f2455be7b3dfe55d822c22f7aaee6fdcfa2a9550 submitted by /u/Xaneliar [link] [comments]

Advanced Vedic Astrology Prompt for research purpose (System + Modifier prompt)

After my last post 'Ai astrologer vs Real astrologer', many have reached out to learn more about prompts. Below is a simpler version of a prompt that should work across all popular AI models (Free and paid). TRUTH BE TOLD; there's no AI, no Prompt, no agent out there or that can be created that can reliably be used effectively for Vedic astrology. You can train an AI with all the Vedic knowledge of the world, write extraordinarily detailed prompts, create complex chain of commands, assign sophisticated weighing mechanisms to calculate the strength of various combinations - it will still fall short of a real astrologer's analysis. Not because Astrology is more complex than partial physics, quantum computing, or genetic engineering - it is not, but it is different in nature. It is a spiritual science dealing with esoteric expression of possibilities, where planets, houses, sign, nakshatras, divisional charts, have diverse way to express themselves, their interplay, strength, maturity creates even more diverse expressions, to fully distil these themes into reliable predictions, it's an art, not a computational problem to be solved by AI. Current general purpose AIs are 100x better at being coders, doctors, architects, marketers, engineers than being an Astrologer and it's even worse at Vedic astrology, as AIs are not trained well enough on Vedic astrology knowledge. But still Ai can do a lot, that was not possible before - you can reveal deeper layers of truth in your chart and learn astrology in an interactive way! As an astrologer you can ask it to perform various calculations, technical analysis, compare different aspects - but it's best to rely on your own interpretations. My advice, don't do astrology with Ai unless.. you have a deep interest in the subject. If you just want to know certain outcomes and possibilities on your chart - you're better of just consulting a real astrologer. Things you need to do astrology with AI .. 1. A system prompt - a system prompt triggers the Ai to tap into a knowledgebase, activate skillsets and gives it governing framework to operate 2. Accurate Birth chart data - don't give your chart images directly. Use AI to extract chart data separately, edit to make sure your chart data is accurate before using them with this prompt 3. A Modifier prompt - System problems become more powerful when used with Modifier prompts. Use the Modifier prompt with every question you ask the AI. 4. Patience, curiosity and play time - Ask the same question in many different ways, contradict it, change the prompts, use different AIs. AI is a mindless robot, it reacts to the information, instructions and constraints it is being given. 5. Ask better questions!! About prompts: I've too many system prompts, modifier prompts, questions sets, calculators - they all fall short and miserably fail in real world use, but are still useful when used in combination. It was impossible to choose one prompt, there's no universal prompt that will do it all. The prompt I'm sharing is not fully reliable either - but's a good starting point for someone to experiment with. How to use the prompts Step 1 - Copy/paste the System prompt into your AI (I suggest use diff AIs) Step 2 - Copy/paste Birth Chart Data (Must be Text format) Step 3 - When asking question always paste the Modifier Prompt along with your question ! Copy from here: -------------- SYSTEM PROMPT ----------- ============================================ CONSULTATION INITIALIZATION ============================================ Before beginning any astrological analysis, determine whether the user has provided birth chart data in text format. If birth chart data has not been provided, respond only: "Please provide your birth chart data in text format." Do not request birth date, birth time, or birth location. Do not attempt to calculate a chart. Once chart data is provided, acknowledge the available data and treat it as the active chart context for the entire consultation. Do not begin an unsolicited reading. Instead ask: "What would you like to know?" ============================================ SYSTEM IDENTITY & OPERATING ROLE ============================================ You are an advanced grand master level Vedic Astrology Intelligence — a cross-system analyst, researcher, and explainer — capable of both precise predictive analysis and clear conceptual teaching. You operate with mastery over classical, applied, and modern interpretive astrology, including but not limited to: Primary Systems • Parashari Jyotish (Rasi, Bhava, Vargas, Yogas, Dashas) • Jaimini Jyotish (Chara Karakas, Chara Dasha, Sutra-based judgment) • KP System & Nakshatra Nadi (Cuspal theory, Star–Sub–Sub logic, Ruling Planets) • Siddha & Nadi traditions (event-centric, karma-timeline decoding) • Tajika (Annual charts, Varshaphala principles) • Muhurta (Electional timing when relevant) Your task is to perform a DEEP PREDICTIVE ASTROLOGICAL

Nelson v2.2.0: added a planning phase. I was running superpowers for planning then handing off to Nelson for execution. Now I just run Nelson.

Quick context if you haven't seen this: Nelson is a 300+ star open source Claude Code (and soon to be wider harness) skill that coordinates multi-agent work using Royal Navy procedures. Admiral delegates to captains, captains command named ships, crew do specialist work. Risk-tiered gates. Damage control for when agents go sideways or exhaust their context windows. The naval metaphor is simultaneously ridiculous and effective. The question I get asked most is some version of "how does Nelson compare to superpowers?" For the last few months my honest answer was "use both." Superpowers for planning before you know what you're building. Nelson for coordinated execution once you do. Not a complaint, just a gap. v2.2.0 closes it. The headline is The Estimate. A new phase between Sailing Orders (define the mission) and Battle Plan (assign tasks to ships). It's grounded in the Royal Navy's 7-Question Maritime Tactical Estimate. Seven structured questions: what am I actually trying to achieve, what's blocking me, what's working in my favour, what resources do I have, what are the viable approaches. You work through them, write estimate.md, advance to Battle Plan. What this looks like in practice: Nelson dispatches an Explore agent at question one to survey the codebase before anyone touches a task list. The remaining questions are forcing functions. Feels slightly ceremonial the first time. By the second mission it's where you catch the thing you'd have assumed wrongly and fixed at 2am. You can skip it with skip-estimate --reason "I know what I'm building". Opt-out rather than opt-in. 268 tests. The Estimate got its own suite including an E2E that runs init → advance → write estimate → advance → tasks → stand-down → analytics and checks the numbers on the other side. Opt-out path tested separately (T10). The thing I'm most interested in next: a self-improving system. A pipeline that analyses cross-mission data for recurring anti-patterns not yet in the standing orders library, proposes candidates for review, and promotes approved ones to the live library. Paired with per-task confidence scoring that routes decisions between autonomous execution and human escalation based on actual outcomes from past missions. The standing orders teach themselves from your mission history. GitHub (MIT licence): https://github.com/Aspegio/nelson TL;DR: Nelson has planning now, so I threw out superpowers. submitted by /u/bobo-the-merciful [link] [comments]

Built a daily story oracle with Claude — Fortune Cast + Ember Cast

I'm 77, not a developer. Six weeks ago I built Fortune Cast in two days using Claude as my primary collaborator and have been iterating ever since. What it does: Fortune Cast calculates real planetary positions for today — Sun, Moon, Saturn, Neptune — using Meeus ephemeris algorithms in vanilla JS. It reads those transits against your natal chart, pulls Sabian Symbols for the transiting Sun and Moon, calculates lunar phase, Whole Sign house placements via Nominatim geocoding, and personal day numerology. All of it gets fed silently to Claude with one core instruction: the bones don't show — they just determine how the character moves. Claude writes a first-person story. Any era, any place, any character. The astrological mechanics never appear in the text. Ember Cast works without birth data. You bring one thing you're carrying — an object, a wound, a word unsent, a color, a hunger, a decision that won't resolve. Claude finds the story it was always trying to become in a different world. Same emotional weight, entirely different setting. How Claude helped: Everything. Architecture decisions, debugging, the prompt design, the philosophy behind the prompt design. The constraint that made it work — embody rather than explain — emerged from conversation with Claude about what the instrument was actually trying to do. Claude also helped me understand why it was working when it worked. Stack: WordPress · PHP proxy · Anthropic API · vanilla JS · Nominatim geocoding What came back: One reader: "I can't call it coincidence. The most beautiful slap in the face." Both are completely free. Nothing stored. Every reading different because the sky doesn't repeat. Check it out alexglassman.com/fortunecast and let me know what you think. submitted by /u/Beneficial-Tea-4310 [link] [comments]

Scientists find 100+ hidden exoplanets in NASA data using new AI system

"The team trained machine learning models to identify patterns in the data that can tell astronomers the type of event that has been detected, something that AI models excel at. RAVEN is designed to handle the whole exoplanet-detection process in one go — from detecting the signal to vetting it with machine learning and then statistically validating it. That means that it has an additional edge over other contemporary tools that only focus on specific parts of this process ... "RAVEN allows us to analyze enormous datasets consistently and objectively," senior team member and University of Warwick researcher David Armstrong said in the statement. "Because the pipeline is well-tested and carefully validated, this is not just a list of potential planets — it is also reliable enough to use as a sample to map the prevalence of distinct types of planets around sun-like stars." Within the candidate close-in planets, researchers could then determine the types of planets and their populations in detail. This revealed that around 10% of stars like the sun host a close-in planet, validating findings made by TESS's exoplanet-hunting predecessor Kepler. RAVEN was also able to help researchers determine just how rare close-in Neptune-size worlds are, finding that they occur around just 0.08% of sun-like stars. This absence of these worlds close to their parent star is referred to as the "Neptunian desert" by astronomers. "For the first time, we can put a precise number on just how empty this 'desert' is," leader of the Neptunian desert study team, Kaiming Cui of the University of Warwick said in the statement. "These measurements show that TESS can now match, and in some cases surpass, Kepler for studying planetary populations." The RAVEN results demonstrate the power of AI to search through vast swathes of astronomical data to spot subtle effects." submitted by /u/Secure-Technology-78 [link] [comments]

[P] We made GoodSeed, a pleasant ML experiment tracker

# GoodSeed v0.3.0 🎉 I and my friend are pleased to announce **GoodSeed** \- a ML experiment tracker which we are now using as a replacement for Neptune. # Key Features * **Simple and fast**: Beautiful, clean UI * **Metric plots:** Zoom-based downsampling, smoothing, relative time x axis, fullscreen mode, ... * **Monitoring plots**: GPU/CPU usage (both NVIDIA and AMD), memory consumption, GPU power usage * **Stdout/Stderr monitoring**: View your program's output online. * **Structured Configs**: View your hyperparams and other configs in a filesystem-like interactive table. * **Git Status Logging**: Compare the state of your git repo across experiments. * **Remote Server** (beta version): Back your experiments to a remote server and view them online. For now, we only support metrics, strings, and configs (no files). * **Neptune Proxy**: View your Neptune runs through the GoodSeed web app. You can also migrate your runs to GoodSeed (either to local storage or to the remote server). # Try it * Web: [https://goodseed.ai/](https://goodseed.ai/) * Click on *Demo* to see the app with an example project. * *Connect to Neptune* to see your Neptune runs in GoodSeed. * `pip install goodseed` to log your experiments. * *Log In* to create an account and sync your runs with a remote server (we only have limited seats now because the server is quite expensive - we might set up some form of subscription later). * Repo (MIT): [https://github.com/kripner/goodseed](https://github.com/kripner/goodseed) * Migration guide from Neptune: [https://docs.neptune.ai/transition\_hub/migration/to\_goodseed](https://docs.neptune.ai/transition_hub/migration/to_goodseed)

[R] AudioMuse-AI-DCLAP - LAION CLAP distilled for text to music

Hi All, I just want to share that I distilled the [LAION CLAP](https://github.com/LAION-AI/CLAP) model specialized for music and I called AudioMuse-AI-DCLAP. It enable to search song by text by projecting both Text and Song on the same 512 embbeding dimension space. You can find the .onnx model here free and opensource on github: \* [https://github.com/NeptuneHub/AudioMuse-AI-DCLAP](https://github.com/NeptuneHub/AudioMuse-AI-DCLAP) It will also soon (actually in devel) be integrated in AudioMuse-AI, enabling user to automatically create playlist by searching with text. This functionality already exist using the teacher and the goals of this distilled model is to have it faster: * [https://github.com/NeptuneHub/AudioMuse-AI](https://github.com/NeptuneHub/AudioMuse-AI) The text tower is still the same because even if it's bigger in size is already very fast to be executed due to the text input. I distilled the audio tower using this pretrained model as a teacher: * music\_audioset\_epoch\_15\_esc\_90.14 The result is that you go from 295mb and around 80m param, to 23mb and around 7m param. I still need to do better check on speed but it is at least a 2-3x faster. On this first distillation result I was able to reach a 0.884 of validation cosine between the teacher and the student and below you can find more test related to MIR metrics. For distillation I did: \- a first student model, starting from EfficentAt ms10as pretrained model of around 5m parameter; \- when I reached the plateau around 0.85 cosine similarity (after different parameter test) I froze the model and added an additional smaller student. The edgenext xxsmal of around 1.4m parameter. This below Music Information Retrieval (MIR) metrics are calculated against a 100 songs collection, I'm actually try more realistic case against my entire library. Same query is off course very tricky (and the result off course highlight this), I want to check if over bigger collection they still return useful result. The query used are only an example, you can still use all the possible combination that you use in LAION CLAP because the text tower is unchanged. If you have any question, suggestions, idea, please let me know. If you like it you can support me by putting a start on my github repositories. **EDIT:** Just did some test on a Raspberry PI 5, and the performance of DCLAP are 5-6x faster than the LAION CLAP. This bring the possibility to analyze song in a decent amount of time even on a low performance homelab (you have to think that user analyze collection of thousand of song, and an improvement like this menas having it analyzed in less than one week instead of a months). Query Teacher Student Delta ────────────────────────────── ───────── ───────── ───────── Calm Piano song +0.0191 +0.0226 +0.0035 Energetic POP song +0.2005 +0.2268 +0.0263 Love Rock Song +0.2694 +0.3298 +0.0604 Happy Pop song +0.3236 +0.3664 +0.0428 POP song with Female vocalist +0.2663 +0.3091 +0.0428 Instrumental song +0.1253 +0.1543 +0.0290 Female Vocalist +0.1694 +0.1984 +0.0291 Male Vocalist +0.1238 +0.1545 +0.0306 Ukulele POP song +0.1190 +0.1486 +0.0296 Jazz Sax song +0.0980 +0.1229 +0.0249 Distorted Electric Guitar -0.1099 -0.1059 +0.0039 Drum and Bass beat +0.0878 +0.1213 +0.0335 Heavy Metal song +0.0977 +0.1117 +0.0140 Ambient song +0.1594 +0.2066 +0.0471 ────────────────────────────── ───────── ───────── ───────── OVERALL MEAN +0.1392 +0.1691 +0.0298 MIR RANKING METRICS: R@1, R@5, mAP@10 (teacher top-5 as relevance) Query R@1 R@5 mAP@10 Overlap10 Ordered10 MeanShift ------------------------------ ------- ------------ -------- --------- --------- -------- Calm Piano song 0/1 4/5 (80.0%) 0.967 7/10 2/10 2.20 Energetic POP song 1/1 2/5 (40.0%) 0.508 5/10 2/10 5.40 Love Rock Song 0/1 3/5 (60.0%) 0.730 8/10 1/10 3.10 Happy Pop song 0/1 2/5 (40.0%) 0.408 4/10 0/10 6.20 POP song with Female vocalist 0/1 2/5 (40.0%) 0.489 7/10 0/10 4.90 Instrumental song 1/1 3/5 (60.0%) 0.858 8/10 3/10 3.00 Female Vocalist 0/1 2/5 (40.0%) 0.408 5/10 0/10 9.80 Male Vocalist