OpenAI is acquiring Neptune to deepen visibility into model behavior and strengthen the tools researchers use to track experiments and monitor trainin
Neptune is praised for its robust machine learning experiment tracking capabilities, earning generally high ratings across reviews with many users highlighting its user-friendly interface and effective tracking capabilities. However, some users express moderate dissatisfaction, indicating room for improvement in certain areas. The sentiment around pricing is not clearly expressed, but users transitioning to alternatives like GoodSeed suggest potential price-related concerns. Overall, Neptune maintains a good reputation in the industry, though it faces competition from newer, simpler tools.
Mentions (30d)
1
Avg Rating
4.2
16 reviews
Platforms
2
Sentiment
10%
1 positive
Neptune is praised for its robust machine learning experiment tracking capabilities, earning generally high ratings across reviews with many users highlighting its user-friendly interface and effective tracking capabilities. However, some users express moderate dissatisfaction, indicating room for improvement in certain areas. The sentiment around pricing is not clearly expressed, but users transitioning to alternatives like GoodSeed suggest potential price-related concerns. Overall, Neptune maintains a good reputation in the industry, though it faces competition from newer, simpler tools.
Features
Use Cases
Industry
information technology & services
Employees
71
Funding Stage
Merger / Acquisition
Total Funding
$12.7M
[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)
View originalPricing found: $122
g2
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]
View originalBuilt 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]
View originalScientists 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]
View original[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)
View original[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
View originalPricing found: $122
Neptune has an average rating of 4.2 out of 5 stars based on 16 reviews from G2, Capterra, and TrustRadius.
Key features include: Experiment tracking, Model versioning, Collaboration tools, Visualization of metrics, Hyperparameter tuning, Integration with popular ML frameworks, Data versioning, Custom dashboards.
Neptune is commonly used for: Tracking model performance over time, Collaborating on ML projects with teams, Visualizing training metrics for analysis, Managing multiple experiments simultaneously, Conducting hyperparameter optimization, Versioning datasets and models for reproducibility.
Neptune integrates with: TensorFlow, PyTorch, Keras, Scikit-learn, MLflow, Jupyter Notebooks, Slack, GitHub, AWS S3, Google Cloud Storage.
