Plutonic Rainbows

I’ve upgraded my application by integrating a transformer-based model for intent classification, which moves beyond the basic, rule-based system I used initially. Now, instead of relying on simple keyword checks, my app calls a smaller, efficient DistilBERT model that can pick up on more nuanced language patterns. This change makes my pipeline more sophisticated and better prepared for future improvements, such as fine-tuning on my own dataset to achieve domain-specific accuracy.

In addition, I’ve tackled the stability and resource issues I faced before by using a smaller model and explicitly setting it to run on the CPU. This reduces the risk of crashes or silent failures. I’ve also maintained my spaCy-based entity extraction and GPT‑4 integration for generating insights, so my app still returns refined prompts and thorough AI responses. Overall, I feel that my setup is now more robust, extensible, and aligned with best practices in modern NLP.

Now available for Pro users, with Plus users gaining access next week. I tested it through the API — it’s impressive but significantly more expensive than other models. Hopefully, the cost will decrease soon.

• I have added wan-i2v templates for file upload and video generation.

• Open AI have added ten requests a month to Deep Research for Plus Users.

• Started building Prompt Refiner application.

• Built application launcher for Flux.1 [Dev] templates.

I discovered that my app was failing to display the generated video because I was incorrectly extracting the video URL from the Fal.ai API response. Initially, my code assumed the video data was inside a property called data (i.e., final_obj.data), but in reality, the final result was returned directly as a plain dictionary in final_obj with the structure {"video": {"url": "..."}}. Once I logged the final API response, I realised I needed to use final_obj directly to extract the video URL. This change fixed the issue, and now the correct URL is passed to the template, allowing the video to display as intended.

Another image-to-video model, this time wan-i2v which claims to be the next evolution in video generation.

Built upon the mainstream diffusion transformer paradigm, Wan2.1 achieves significant advancements in generative capabilities through a series of innovations, including our novel spatio-temporal variational autoencoder (VAE), scalable pre-training strategies, large-scale data construction, and automated evaluation metrics. These contributions collectively enhance the model’s performance and versatility

Trying to figure out a way to open links in new tabs.

Latest album from New Mexican Stargazers.

It slowly overtook the front of my house, almost like a reminder of things I needed to accomplish but never had enough energy to do. in fact, if I was living alone, there is no telling how long it would have continued to grow for. I had taken some pictures of the beautiful, yet intrusive display of residential flora, and it was the only the very next day that it had been removed. out of my life, finally allowing for light to enter my room once more.

Almost 90 minutes of drifting ambience that recalls the mind-expanding New Age albums of the late 1980s.

Buy here

I was incredibly excited about this film adaptation of Stephen King’s short story. I must have read this creepy, classic tale at least a dozen times over the past thirty-plus years. It started off well but lost momentum after thirty or forty minutes. It strayed too far from the original and lacked the same haunting quality as the short story. For me, it was incredibly disappointing.

I updated the spreadsheets for my 2025 savings plan, discovered an insightful book on syntax, completed a new image generation app while resolving a metadata issue, and began organizing my finances.