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Show HN: TubePen – My attempt to get more out of YouTube learning

Hi HN! I made this because I always forget what I'm trying to learn from YouTube.<p>Test yourself: Can you remember the main concepts from the last (educational) video you watched?<p>So, why not highlight and take notes on YouTube videos, just like in books? That's TubePen.<p>Sign in, replace "youtube" with "tubepen" in your YouTube URL, and you're ready to retain more from your videos.<p>I’d love your feedback! What do you think of my landing page? Use the 10-day free trial and see if it’s useful for you.<p>Thanks!

Show HN: 3D Terrain simulation for hiking, skiing etc.

I'm working on a GPS track visualizer for quite some time. It shines in hilly and mountaineous terrain (where a 3D view makes more sense), but it also offers quite a lot of Strava-like features (statistics etc.).<p>You can upload your GPX and FIT files manually, or sync directly with Garmin, Coros and Polar.<p>See <a href="https://cubetrek.com" rel="nofollow">https://cubetrek.com</a> for the live app and check out some examples there. It's free and opens source.<p>Also, anyone who likes to work with 3D visualizations (especially Babylon.js), let me know if you like to help polish this thing further.

Show HN: 3D Terrain simulation for hiking, skiing etc.

I'm working on a GPS track visualizer for quite some time. It shines in hilly and mountaineous terrain (where a 3D view makes more sense), but it also offers quite a lot of Strava-like features (statistics etc.).<p>You can upload your GPX and FIT files manually, or sync directly with Garmin, Coros and Polar.<p>See <a href="https://cubetrek.com" rel="nofollow">https://cubetrek.com</a> for the live app and check out some examples there. It's free and opens source.<p>Also, anyone who likes to work with 3D visualizations (especially Babylon.js), let me know if you like to help polish this thing further.

Show HN: 3D Terrain simulation for hiking, skiing etc.

I'm working on a GPS track visualizer for quite some time. It shines in hilly and mountaineous terrain (where a 3D view makes more sense), but it also offers quite a lot of Strava-like features (statistics etc.).<p>You can upload your GPX and FIT files manually, or sync directly with Garmin, Coros and Polar.<p>See <a href="https://cubetrek.com" rel="nofollow">https://cubetrek.com</a> for the live app and check out some examples there. It's free and opens source.<p>Also, anyone who likes to work with 3D visualizations (especially Babylon.js), let me know if you like to help polish this thing further.

Show HN: 3D Terrain simulation for hiking, skiing etc.

I'm working on a GPS track visualizer for quite some time. It shines in hilly and mountaineous terrain (where a 3D view makes more sense), but it also offers quite a lot of Strava-like features (statistics etc.).<p>You can upload your GPX and FIT files manually, or sync directly with Garmin, Coros and Polar.<p>See <a href="https://cubetrek.com" rel="nofollow">https://cubetrek.com</a> for the live app and check out some examples there. It's free and opens source.<p>Also, anyone who likes to work with 3D visualizations (especially Babylon.js), let me know if you like to help polish this thing further.

Show HN: A Better Log Service

Hello everyone, there are many log services available and this is my attempt at a better one.<p>Most online logging tools feature convoluted UIs, arbitrary mandatory fields, questionable AI/insights, complex pricing, etc. I hope my application fixes most of these issues. It also has some nice features, such as automatic Geo IP checks and public dashboards.<p>Although I've created lots of software, this is my first open source application (MIT license), the tutorial for selfhosting is hopefully sufficient! Most of my development career has been with C#, NodeJS and PHP. For this project I've used PHP (8.3) which is an absolute joy to work with. The architecture is very scalable, but I've only tested up to a few billion logs. The current version is used in production for a few months now. Hope you enjoy/fork it as you see fit!

Show HN: A Better Log Service

Hello everyone, there are many log services available and this is my attempt at a better one.<p>Most online logging tools feature convoluted UIs, arbitrary mandatory fields, questionable AI/insights, complex pricing, etc. I hope my application fixes most of these issues. It also has some nice features, such as automatic Geo IP checks and public dashboards.<p>Although I've created lots of software, this is my first open source application (MIT license), the tutorial for selfhosting is hopefully sufficient! Most of my development career has been with C#, NodeJS and PHP. For this project I've used PHP (8.3) which is an absolute joy to work with. The architecture is very scalable, but I've only tested up to a few billion logs. The current version is used in production for a few months now. Hope you enjoy/fork it as you see fit!

Show HN: A Better Log Service

Hello everyone, there are many log services available and this is my attempt at a better one.<p>Most online logging tools feature convoluted UIs, arbitrary mandatory fields, questionable AI/insights, complex pricing, etc. I hope my application fixes most of these issues. It also has some nice features, such as automatic Geo IP checks and public dashboards.<p>Although I've created lots of software, this is my first open source application (MIT license), the tutorial for selfhosting is hopefully sufficient! Most of my development career has been with C#, NodeJS and PHP. For this project I've used PHP (8.3) which is an absolute joy to work with. The architecture is very scalable, but I've only tested up to a few billion logs. The current version is used in production for a few months now. Hope you enjoy/fork it as you see fit!

Show HN: A Better Log Service

Hello everyone, there are many log services available and this is my attempt at a better one.<p>Most online logging tools feature convoluted UIs, arbitrary mandatory fields, questionable AI/insights, complex pricing, etc. I hope my application fixes most of these issues. It also has some nice features, such as automatic Geo IP checks and public dashboards.<p>Although I've created lots of software, this is my first open source application (MIT license), the tutorial for selfhosting is hopefully sufficient! Most of my development career has been with C#, NodeJS and PHP. For this project I've used PHP (8.3) which is an absolute joy to work with. The architecture is very scalable, but I've only tested up to a few billion logs. The current version is used in production for a few months now. Hope you enjoy/fork it as you see fit!

Show HN: TypeScript/React/Vue Window Layout Manager (Tabs, Floating, Popouts)

Show HN: TypeScript/React/Vue Window Layout Manager (Tabs, Floating, Popouts)

Show HN: TypeScript/React/Vue Window Layout Manager (Tabs, Floating, Popouts)

Show HN: TypeScript/React/Vue Window Layout Manager (Tabs, Floating, Popouts)

Show HN: Freeact – A Lightweight Library for Code-Action Based Agents

Hello! We just released freeact (<a href="https://github.com/gradion-ai/freeact">https://github.com/gradion-ai/freeact</a>), a lightweight agent library that empowers language models to act as autonomous agents through executable code actions.<p>By enabling agents to express their actions directly in code rather than through constrained formats like JSON, freeact provides a flexible and powerful approach to solving complex, open-ended problems that require dynamic solution paths.<p>* Supports dynamic installation and utilization of Python packages at runtime<p>* Agents learn from feedback and store successful code actions as reusable skills in long-term memory<p>* Skills can be interactively developed and refined in collaboration with freeact agents<p>* Agents compose skills and any other Python modules to build increasingly sophisticated capabilities<p>* Code actions are executed in ipybox (<a href="https://github.com/gradion-ai/ipybox">https://github.com/gradion-ai/ipybox</a>), a secure Docker + IPython sandbox that runs locally or remotely<p>GitHub repo: <a href="https://github.com/gradion-ai/freeact">https://github.com/gradion-ai/freeact</a><p>Evaluation: <a href="https://gradion-ai.github.io/freeact/evaluation/" rel="nofollow">https://gradion-ai.github.io/freeact/evaluation/</a><p>See it in action: <a href="https://github.com/user-attachments/assets/83cec179-54dc-456c-b647-ea98ec99600b">https://github.com/user-attachments/assets/83cec179-54dc-456...</a><p>We'd love to hear your feedback!

Show HN: Boulette - Protect you from yourself (even as root).

Show HN: TabPFN v2 – A SOTA foundation model for small tabular data

I am excited to announce the release of TabPFN v2, a tabular foundation model that delivers state-of-the-art predictions on small datasets in just 2.8 seconds for classification and 4.8 seconds for regression compared to strong baselines tuned for 4 hours. Published in Nature, this model outperforms traditional methods on datasets with up to 10,000 samples and 500 features.<p>The model is available under an open license: a derivative of the Apache 2 license with a single modification, adding an enhanced attribution requirement inspired by the Llama 3 license: <a href="https://github.com/PriorLabs/tabpfn">https://github.com/PriorLabs/tabpfn</a>. You can also try it via API: <a href="https://github.com/PriorLabs/tabpfn-client">https://github.com/PriorLabs/tabpfn-client</a><p>TabPFN v2 is trained on 130 million synthetic tabular prediction datasets to perform in-context learning and output a predictive distribution for the test data points. Each dataset acts as one meta-datapoint to train the TabPFN weights with SGD. As a foundation model, TabPFN allows for fine-tuning, density estimation and data generation.<p>Compared to TabPFN v1, v2 now natively supports categorical features and missing values. TabPFN v2 performs just as well on datasets with or without these. It also handles outliers and uninformative features naturally, problems that often throw off standard neural nets.<p>TabPFN v2 performs as well with half the data as the next best baseline (CatBoost) with all the data.<p>We also compared TabPFN to the SOTA AutoML system AutoGluon 1.0. Standard TabPFN already outperforms AutoGluon on classification and ties on regression, but ensembling multiple TabPFNs in TabPFN v2 (PHE) is even better.<p>There are some limitations: TabPFN v2 is very fast to train and does not require hyperparameter tuning, but inference is slow. The model is also only designed for datasets up to 10k data points and 500 features. While it may perform well on larger datasets, it hasn't been our focus.<p>We're actively working on removing these limitations and intend to release new versions of TabPFN that can handle larger datasets, have faster inference and perform in additional predictive settings such as time-series and recommender systems.<p>We would love for you to try out TabPFN v2 and give us your feedback!

Show HN: TabPFN v2 – A SOTA foundation model for small tabular data

I am excited to announce the release of TabPFN v2, a tabular foundation model that delivers state-of-the-art predictions on small datasets in just 2.8 seconds for classification and 4.8 seconds for regression compared to strong baselines tuned for 4 hours. Published in Nature, this model outperforms traditional methods on datasets with up to 10,000 samples and 500 features.<p>The model is available under an open license: a derivative of the Apache 2 license with a single modification, adding an enhanced attribution requirement inspired by the Llama 3 license: <a href="https://github.com/PriorLabs/tabpfn">https://github.com/PriorLabs/tabpfn</a>. You can also try it via API: <a href="https://github.com/PriorLabs/tabpfn-client">https://github.com/PriorLabs/tabpfn-client</a><p>TabPFN v2 is trained on 130 million synthetic tabular prediction datasets to perform in-context learning and output a predictive distribution for the test data points. Each dataset acts as one meta-datapoint to train the TabPFN weights with SGD. As a foundation model, TabPFN allows for fine-tuning, density estimation and data generation.<p>Compared to TabPFN v1, v2 now natively supports categorical features and missing values. TabPFN v2 performs just as well on datasets with or without these. It also handles outliers and uninformative features naturally, problems that often throw off standard neural nets.<p>TabPFN v2 performs as well with half the data as the next best baseline (CatBoost) with all the data.<p>We also compared TabPFN to the SOTA AutoML system AutoGluon 1.0. Standard TabPFN already outperforms AutoGluon on classification and ties on regression, but ensembling multiple TabPFNs in TabPFN v2 (PHE) is even better.<p>There are some limitations: TabPFN v2 is very fast to train and does not require hyperparameter tuning, but inference is slow. The model is also only designed for datasets up to 10k data points and 500 features. While it may perform well on larger datasets, it hasn't been our focus.<p>We're actively working on removing these limitations and intend to release new versions of TabPFN that can handle larger datasets, have faster inference and perform in additional predictive settings such as time-series and recommender systems.<p>We would love for you to try out TabPFN v2 and give us your feedback!

Show HN: TabPFN v2 – A SOTA foundation model for small tabular data

I am excited to announce the release of TabPFN v2, a tabular foundation model that delivers state-of-the-art predictions on small datasets in just 2.8 seconds for classification and 4.8 seconds for regression compared to strong baselines tuned for 4 hours. Published in Nature, this model outperforms traditional methods on datasets with up to 10,000 samples and 500 features.<p>The model is available under an open license: a derivative of the Apache 2 license with a single modification, adding an enhanced attribution requirement inspired by the Llama 3 license: <a href="https://github.com/PriorLabs/tabpfn">https://github.com/PriorLabs/tabpfn</a>. You can also try it via API: <a href="https://github.com/PriorLabs/tabpfn-client">https://github.com/PriorLabs/tabpfn-client</a><p>TabPFN v2 is trained on 130 million synthetic tabular prediction datasets to perform in-context learning and output a predictive distribution for the test data points. Each dataset acts as one meta-datapoint to train the TabPFN weights with SGD. As a foundation model, TabPFN allows for fine-tuning, density estimation and data generation.<p>Compared to TabPFN v1, v2 now natively supports categorical features and missing values. TabPFN v2 performs just as well on datasets with or without these. It also handles outliers and uninformative features naturally, problems that often throw off standard neural nets.<p>TabPFN v2 performs as well with half the data as the next best baseline (CatBoost) with all the data.<p>We also compared TabPFN to the SOTA AutoML system AutoGluon 1.0. Standard TabPFN already outperforms AutoGluon on classification and ties on regression, but ensembling multiple TabPFNs in TabPFN v2 (PHE) is even better.<p>There are some limitations: TabPFN v2 is very fast to train and does not require hyperparameter tuning, but inference is slow. The model is also only designed for datasets up to 10k data points and 500 features. While it may perform well on larger datasets, it hasn't been our focus.<p>We're actively working on removing these limitations and intend to release new versions of TabPFN that can handle larger datasets, have faster inference and perform in additional predictive settings such as time-series and recommender systems.<p>We would love for you to try out TabPFN v2 and give us your feedback!

Show HN: TabPFN v2 – A SOTA foundation model for small tabular data

I am excited to announce the release of TabPFN v2, a tabular foundation model that delivers state-of-the-art predictions on small datasets in just 2.8 seconds for classification and 4.8 seconds for regression compared to strong baselines tuned for 4 hours. Published in Nature, this model outperforms traditional methods on datasets with up to 10,000 samples and 500 features.<p>The model is available under an open license: a derivative of the Apache 2 license with a single modification, adding an enhanced attribution requirement inspired by the Llama 3 license: <a href="https://github.com/PriorLabs/tabpfn">https://github.com/PriorLabs/tabpfn</a>. You can also try it via API: <a href="https://github.com/PriorLabs/tabpfn-client">https://github.com/PriorLabs/tabpfn-client</a><p>TabPFN v2 is trained on 130 million synthetic tabular prediction datasets to perform in-context learning and output a predictive distribution for the test data points. Each dataset acts as one meta-datapoint to train the TabPFN weights with SGD. As a foundation model, TabPFN allows for fine-tuning, density estimation and data generation.<p>Compared to TabPFN v1, v2 now natively supports categorical features and missing values. TabPFN v2 performs just as well on datasets with or without these. It also handles outliers and uninformative features naturally, problems that often throw off standard neural nets.<p>TabPFN v2 performs as well with half the data as the next best baseline (CatBoost) with all the data.<p>We also compared TabPFN to the SOTA AutoML system AutoGluon 1.0. Standard TabPFN already outperforms AutoGluon on classification and ties on regression, but ensembling multiple TabPFNs in TabPFN v2 (PHE) is even better.<p>There are some limitations: TabPFN v2 is very fast to train and does not require hyperparameter tuning, but inference is slow. The model is also only designed for datasets up to 10k data points and 500 features. While it may perform well on larger datasets, it hasn't been our focus.<p>We're actively working on removing these limitations and intend to release new versions of TabPFN that can handle larger datasets, have faster inference and perform in additional predictive settings such as time-series and recommender systems.<p>We would love for you to try out TabPFN v2 and give us your feedback!

Show HN: LA Wildfire Satellite Analysis