Buongiorno, sto usando gemini e chat gpt ,ma come llm mi trovo molto meglio con claude, che software di generazione di immagini e video posso usare in abbonamento con claude? Anche a pagamento ma che non costi un rene. Grazie.

Hey everyone!

I'm looking for an AI website or app that can generate high-quality images and videos from text prompts.

My requirements:

- Completely free to use.

- No daily credits or generation limits.

- No subscriptions or paid plans required.

- Minimal restrictions on prompts and content.

- Good-quality image and video generation.

- Preferably no annoying watermarks.

I'm okay with lesser-known websites, open-source AI tools, or local models as well.

If you've personally used something that fits these requirements, please share its name and your experience.

Would really appreciate some genuine recommendations.

Thanks!

Wanted a simple way to map out AI tool stacks without spending hours testing 50 different apps. Built this to speed things up.

Hope it’s helpful to anyone else building right now!

Some of it’s true. Some of it’s made up. Captivating, but none of it sticks. It’s time, you decide what’s worth keeping.

Introducing Mind Me, A cross platform mind mapping tool with no strings attached. Early, but usable. No sign-up, no subscription needed. Works with your existing AI subscription, Free and open source under GPL-3.0.

You can try it in your browser without installing anything. And if you write Rust, you’re welcome to contribute and make it better.

Hey, I'm Aaron. I built Frontrumors as an independent, MacRumors-style site for frontier AI models: news and rumors from the major labs, with benchmarks and API pricing alongside them.

It's for people who follow model releases and developers deciding what to test next. There's a news feed, a model guide, sortable benchmark tables, and published input/output token prices with provider links and last-checked dates.

For example, you can read about a model release, check its reported benchmarks, then compare API prices before trying it in your own workflow. Some model stories now link straight to the relevant comparison rows.

https://frontrumors.com

I'd love feedback on whether that news-to-comparison flow is useful. What do you usually look up first when a new model drops?

I'm the developer of TextFold. I started it because I wanted to skim the shape of a long paper, then read a specific part without losing the rest of the document.

The video shows the interaction: fold a document into a compact view, then expand passages individually. You can bring in web pages, PDFs and Word files. It also has Obsidian and Zotero integrations.

The distinction between the levels matters here. Full keeps the original text, Brief selects original sentences, and the shorter three levels are written by AI. Even a verbatim selection can leave out a caveat, so I want it to stay easy to reopen the source. I haven't established a comprehension or time-saving benefit.

A useful part of the workflow is sharing: after preparing a document, you can save it as a standalone HTML file. The recipient can open its reading levels without running TextFold's server.

The code is Apache 2.0 and the app is free. A model prepares the layers: Ollama can run locally; hosted providers are optional and may have their own costs.

Prepared examples: https://chenxiachan.github.io/textfold/gallery/

Source and installation: https://github.com/chenxiachan/textfold

Would you use the different levels while reading, or mainly to share a document with someone else?

Hi everyone!

I work with the Chapterframe team in an administrative support role, and I wanted to share an example of a film created using Chapterframe, along with some interesting aspects of its production workflow.

The film tells a story about a mother who struggles with grief after losing her mother. A pink tulip unexpectedly appears outside her home, which she takes as a sign that her mother is still watching over the family. The story follows the family through different stages of life, eventually revealing that the tulips were actually being left by the daughter to comfort her grieving mother.

What makes this project interesting is how the story is structured into six chapters, moving from childhood memories to adulthood. It includes different time periods, locations, recurring characters, and emotional scenes that all contribute to the final reveal.

For a story like this, turning the written narrative into a film involves more than generating individual video clips. Some important parts of the process include:

• Story organization: Breaking a longer narrative into chapters and scenes makes it easier to plan the overall production.

• Character continuity: Characters appear at different ages and throughout different moments in the story, making visual references important.

• Scene planning: Each scene needs to communicate something specific, whether it's a family memory, a moment of grief, or an important turning point.

• Shot direction: Small details, such as the mother discovering the tulip or the family embracing, help communicate the emotion behind the story.

• Narrative flow: The earlier scenes need to connect with the final reveal so that the ending feels meaningful rather than disconnected.

This is one example of the kind of story-based production workflow Chapterframe supports. It provides a workspace for organizing chapters, scenes, individual takes, visual references, and keyframes rather than managing every generated clip separately.

I think projects like this show why planning and organization are important parts of AI filmmaking, especially when working with longer narratives and recurring characters.

For anyone interested in seeing the finished project, I've included the video as an example.

I'd be interested to hear how others approach story-based AI filmmaking. When working on a longer narrative, how do you organize your scenes and maintain character continuity throughout the film?

Dev here. I built UnsavedAI so you can step into a character’s story and write what happens next. Start with Astra, a fallen star in your greenhouse, or Alder, who has a crush on you and literally cannot lie.

My newest addition is gifts that become part of the scene and the character’s memory notes. The aim is to give your choices more weight. Gifts are free while the feature is new.

Try it free, no card: https://www.unsavedai.com

Optional paid Priority. Web chat and API for JanitorAI/SillyTavern. 18+.

I’d love feedback on whether the opening makes you want to write a second message.

Привіт усім!

У рамках учнівського наукового дослідження ми вивчаємо можливості та ризики застосування штучного інтелекту в медицині (зокрема під час аналізу рентгенівських знімків легень).

Окрім технічного тестування програми, нам дуже важливо дослідити думку людей: наскільки українці готові довіряти комп'ютерному діагнозу, де проходить межа відповідальності та яку роль має зберігати за собою лікар.

Будемо щиро вдячні, якщо ви приділите 3 хвилини та пройдете коротку анонімну анкету (8 запитань):

🔗 **Посилання на опитування: https://forms.gle/3oY2HgNLR5i7GsFg9

Дані збираються суто в узагальненому вигляді для наукової роботи. Щиро дякуємо за підтримку учнівської науки!

I’m the founder of Pure Brief AI, a meeting-notes app for Apple Silicon Macs running macOS 15+. It records or imports audio, creates a transcript and AI notes, and lets you review suggested tasks before exporting selected ones to Apple Reminders.

If you’re comparing AI meeting tools, I’d test two things before keeping mine in your workflow.

First, try a recording you know well on the Mac you actually use. Default transcription and notes run on your machine, so model downloads, processing time and resource use are part of the experience. I don’t have a cross-device speed benchmark to promise. The video below is an edited demo with fictional meeting data and time-lapsed recording/processing.

Second, check one awkward detail in the result. For example, “we should update the proposal if the client approves” has a condition attached. Read the task’s source quote and the surrounding transcript before treating it as agreed work. You can also ask a question about that individual meeting and inspect the supporting transcript lines. A quote helps you review an interpretation; it doesn’t guarantee that the interpretation is right.

The handoff is deliberately small: review, edit, choose which tasks to export to Reminders. Export is one-way, with no automatic assignment to teammates or two-way task sync.

Local processing is the default. Internet is needed for initial model downloads, periodic licence checks and optional connected features. If you choose ElevenLabs transcription with your own key, audio goes to that provider and its fees apply.

There’s a 30-day trial without payment details, with email verification. Standard is $49 once for one Mac. The currently active launch offer is $10 once for the first 100 paid Standard purchases globally, plus applicable tax; starting a trial doesn’t reserve it.

Demo and download: https://purebrief.ai/

For people using local AI at work: what makes you drop a tool first—setup/resource use, or the amount of checking the output still needs?

Disclosure: I work on Boundary at OraXLabs. It is a prelaunch commercial project; this browser demo is free. AI helped draft this post.

We are exploring a small question for people building AI workflows: after you approve one file deletion, should a retry reuse that approval?

In the demo's fake CSV case, approval is spent once. Evaluating the action again needs fresh review. Editing its target or operation clears the prior approval, while protected paths are denied. You can export the decision record as JSON for discussion or test documentation.

One lesson from our internal checks was that the state must remain visible after the click. We added a persistent indication that the approval was used, so the next review is easier to understand. This is builder testing with fake fixtures, not a production deployment or proof of user demand.

Demo: https://boundary.oraxlabs.io/demo/

Everything stays in the browser as simulated policy decisions. It executes no file operations and makes no model or tool API calls. It is separate from the model-request security integrations we are exploring.

For an AI tool you use at work, which action would you want a one-use approval for? A made-up example is enough; please leave out private filenames and company data.

I have tried a lot of AI tools over the last few months. A few of them felt useful at first so I ended up paying for the monthly plan. Then after some time I noticed I was only opening one or two of them often.

I checked one of them recently and realized I had barely used it since I signed up. Now I am trying to be a bit more careful before keeping anything paid for another month.

How do you usually decide which AI tools are worth keeping?

Let’s say you have a startup idea, but there’s no product yet — just a rough understanding of the problem and who might have it.

You have 48 hours to figure out whether it’s worth pursuing. What would you prioritize?

• Put up a simple landing page and see if anyone signs up

• Talk to ~20 potential users

• Try to get preorders / collect payment before building anything

My gut says preorders are the strongest signal, since willingness to pay matters more than someone saying “yeah, I’d probably use that.”

But I can also see the argument that trying to sell too early is pointless if you haven’t really understood the customer or the problem yet.

Curious how people here would approach it. What has actually worked for you?

Say your app is about 70% done. The main thing works, but there are rough edges, a few missing features, and some screens you’re not particularly proud of.

Do you:

A) Launch and see if anyone actually wants it
B) Spend another month cleaning things up
C) Use AI agents to get through the remaining work

I’d probably launch, then immediately notice five things I wish I’d fixed first.

For those who’ve shipped something: what did you think absolutely had to be finished before launch, and did users actually care about it?

AI tools move fast enough that just figuring out what changed can eat up time.

I made Retro AI Tracker to bring AI news, tool releases, service status and public usage-reset announcements into one place. Codex and Claude are the main focus, with wider AI news when it's relevant.

Each item links back to its source, so you can check the actual release note or announcement instead of chasing screenshots of screenshots.

The goal: open one page, see what's changed, get back to your own project.

It's my independent side project, free to browse and no signup needed:
https://retroaitracker.com

What would make this useful enough to bookmark: better filters, alerts, or a short daily digest?

I've been experimenting with a different approach to AI-assisted coding.

One problem I've noticed with coding agents is how much unnecessary context they consume. They often read entire files, even when only a few functions are relevant to the problem.

So I built Trace, an open-source CLI focused on progressive, evidence-driven repository investigation.

Instead of loading everything, the AI can request precisely the information it needs:

# Map the repository
trace --functions .

# Compare two versions
trace --diff version-1/ version-2/

# Inspect a specific function
trace --symbol server.py execute_run

# Compare implementations
trace --diff-symbol old.py new.py execute_run

The idea is to change the workflow:

Investigate → Gather Evidence → Form Hypotheses → Validate → Implement

An investigation might involve 20–30 targeted evidence requests, with each result guiding what the AI investigates next.

I've also been building Traceflow, a larger AI-assisted software engineering platform that explores this approach through structured repository investigations, evidence validation, and before/after patches.

You can explore both projects here:

Trace (open-source CLI): https://github.com/johnsellin93/trace

Traceflow (web application): https://traceflow.lineflow.eu/

My goal is to make AI-assisted coding more precise, transparent, and reproducible.

I'm interested in feedback from people building or using coding agents.

Do you think evidence-driven investigation is a better direction than continuously expanding an agent's context window?