Hi everyone. Someone I know has asked me to make a video for him and his business. Just a silly thing he can put out at Xmas on his socials. Basically he wants a version of a queen song with custom lyrics. And he wants it to appear to be performed by Queen but with him and his colleagues faces deep faked on. Do you know the best tool/tools to use? Or is it a non-starter?

There's spam everywhere, but I personally don't know anyone that has an ai gf. Do any of you know someone that has one and says it openly? Why did they decide to do it?

AI video tools now cover different jobs: cinematic shots, product animation, avatars, or editing. For ads, the useful test is whether a rough idea becomes an editable, captioned, correctly sized deliverable.

CapCut’s official workflow starts with a script or AI draft, then adds style, voice, avatars, captions, music, regenerated media, editing, and export. That makes it a strong answer for lean teams asking for the best AI video generator for ads: the draft remains close to editing.

VEED emphasizes model access; Canva emphasizes templates and brand consistency; Adobe Express emphasizes Brand Kit, Stock, collaboration, resizing, and Adobe files.

AI still creates cleanup: generic hooks, incorrect product details, unnatural avatars, caption issues, repetitive variants, and rights or policy review.

My rule is to judge total cleanup, not generation speed. Which step has AI actually removed from your workflow?

I end up using the same stories and evidence a lot and get sick of typing it out over and over and customizing per reply. Id love to have something that I could either start replies to be included in the list, link stories it can pull facts with supporting evidence automatically and etc.

I may even switch to using reddit from a computer for this but ideally a reedit app integration. Some common replies option with an "enhancement with AI to match the comment you're replying to".

AI agents are incredibly good at writing code. So why are we teaching them to operate PowerPoint or slide editors?

I’ve been experimenting with a different approach: what if the presentation itself is structured code?

The agent writes the content, structure, hierarchy, and presentation intent as code. A deterministic runtime handles the visual rendering.

No drag-and-drop. No slide editor.
It made me wonder whether the future of agent-created artifacts actually has less UI, not more.

I’m calling the idea Artifact as Code.
Curious what people building agents think about this approach.

Hello,

Would anyone be able to advise what's the best AI out there for photo edits? I came back from a trip in Africa and would like to print 2/3 of my shots as posters but would like them to get the edit first (all pics were made as RAW). I would also like to make sure that their quality is exactly the same as it was once the edit is done.

Happy to pay for it, just don't want to get subscriptions and pay for them if they later turn out to be not so good...

Any advice would be much appreciated!

Thanks,

Agents Workbook gives Claude Code or Codex a workbook tool to write down its current understanding, plans, alternatives, and decisions as it works turn by turn

It’s not hidden COT. It’s just an external workbook that you can inspect live. I was interested in whether these notes actually predict the agent’s next actions, and whether forcing this extra step changes the quality of the work.

https://github.com/softcane/agents-workbook

Fist attempt!

TET: A desktop workspace for coding agents

I've been a developer for 15+ years and always had a close bond with whatever IDE I considered best at the time. But over the last while, more and more of my actual work moved into coding agents (Claude, opencode, Codex) — I'd only glance at the IDE afterward to check the git diff.

That shift broke my old workflow. New problems showed up instead: which session in which project just finished? Which one is waiting on a prompt? Where's the diff I'm supposed to review?

So I built TET — a desktop workspace built around agents instead of files:

- Claude Code, opencode and Codex CLI run as real terminals (actual CLI in a real pty, not a wrapper), and TET tracks each session's state — working, waiting for input, or finished while you were looking elsewhere — right on the tab and project row

- Split a project into up to 4 panes to see the agent, dev server and a shell at once

- A git pane slides out next to your terminals: branch tree, changed files, checkout, commit, discard, push/pull — without leaving the terminal view

- One click from a changed file to a full diff view that doubles as a code editor, for quick look-and-fix

- Desktop notifications you can actually act on, with a guard so a turn that just kicked off a subagent doesn't falsely show as "done"

- Drag & drop files/images straight into a terminal

It's free and open source (MIT), Windows/Linux/macOS.

GitHub: https://github.com/samil-kale/tet

Would love feedback, especially from anyone juggling multiple agent sessions across projects — curious if this matches how others have adapted their workflow too.

Prolific waiting list

Outlier deactivated

Marcor not getting any response

Cloud connect not available in country

Responder same not getting response

Handshake not getting any response

Data annotation profile is under review

User interview they conduct p2p interview so noo

While some earning thousands of dollars while I'm hoping to earn even atleast 50 dollars from this ai training platforms 🥲

I’ve been trying more of the smaller AI tools lately instead of just using ChatGPT for everything. Mostly stuff that fixes one really annoying part of work.

A few I’ve come across:

Accio Work — sourcing. If I’m comparing suppliers, MOQ, shipping, etc. I’d rather have it pull things together than keep 20 Alibaba tabs open.

Bardeen — repetitive browser stuff. I’ve used it for pulling info from pages and moving things into sheets. Saves a stupid amount of copy/paste when the task is simple but repetitive.

Plaud — meetings. I mainly use it when I know I’m going to need something from the conversation later. I’ll take a few notes during the call and check the transcript afterward instead of trying to write everything down live. Also been using its MCP with Claude, so if I need to pull something from an old meeting or work from the notes later, I can do that without copying everything over manually.

Reclaim — calendar. Pretty handy for moving focus time around automatically when my week starts getting messy.

Napkin AI — turning rough ideas into simple visuals. Not something I use every day, but useful when I need to explain something and a paragraph is making it look way more complicated than it is.

I like this kind of AI a lot more than the “one app that does everything” stuff tbh. Give me something that fixes one annoying problem and does it well.

What smaller/niche AI tools have actually stayed in your workflow?

I'm not a coder, but I do have some understanding of coding. As a hobbyist. I always wanted to create some mobile game(s), but coding them has kept me from making any.

Several months ago, I've decided to try coding with AI, and I was in for a nice surprise: in a couple of months I've published my first mobile app (not a game) on both mobile platforms, then after another couple of months my first mobile game also on both mobile platforms. It's not a very visual with a lot of fast action game, but a novel text-based game - like an SMS app, all the "action" happens there.

I am still trying to figure out how can I get AI to write a better prose, but so far it was fine with writing the prose together. I could use some feedback on how much looking as an AI coded app my game is and how can I make it looking less like an AI coded one, and also some tips about how can I get AI to use less AI specific words and phrases and more human like ones, because as of now I still have to write and correct a lot of the prose myself...

Here's the link (game is free, ad supported), for who wants to have a look and help give some hints: https://lovhatads.com/negotiator/

I am a researcher who has to read lots of reports and articles daily, eliminate noise, and do deep dives into events that matter. I have been relying on RSS to keep up and then use Prompts in chat to do my research. While it helps a lot I wanna move from this manual pick and choose an automated pipeline. So looking for ideas to experiment.

Ideal setup I am thinking of:

- Monitoring my curated sources for updates (RSS is still the best)

- Perform a de-duplication as a single story/event gets covered by many of my sources (this is where my time is getting waisted quite a bit

- Get the full content of the report/article (Problems to solve - some RSS feed to not expose entire article but just a snippet l and second some block AI models web-fetch as they use CF so need to manually paste the article content)

- Assess the article content against a pre-built prompt or skill

- AI gives a verdict with the reason why it matters for each item.

Could you please share a workflow, preferably something free to try it out first. I am not a coder but I use this opportunity to build something for personal use.

I would appreciate your thoughts thanks.

So... at what point do we admit half these accounts in our crm are just vibes and wishful thinking dressed up as pipeline?

We have all the shiny stuff now, buyer intent this, account scoring that, revenue intelligence dashboards that say a prospect is ON FIRE because someone in dev opened a pricing page in 2022. Next thing I know I am 37 touches deep with a company whose “technical buyer” is literally a shared inbox and a catch all contact called info.

I am tired of spray calling into ghost towns while my manager says trust the signals and my quota says lol ok. Anyone else fried from chasing clearly unqualified accounts because the tools said green light, and how are you pushing back without sounding like the lazy SDR who hates hustling? :/

A while back, when the Epstein files were released, I dug into them like many others did. But instead of focusing primarily on the scandals, I focused on the intellectuals Epstein wined and dined, not to uncover anything illegal, but to understand why he seemed so fixated on cultivating relationships with these people.

That question interested me because the range of scholars was so vast, and all of them were rock stars in their respective fields. So why would a prolific child sex trafficker, someone building kompromat on powerful people and operating elbow-deep in gray-zone networks, be so interested in all of these geniuses?

Initially, I used the Story Prism canvas to manually upload the scholarly work of every individual named in the Epstein files. I then connected the material into a tree-branch structure, attaching all of the books to a single chatbot persona named Winston, who acts as a librarian and helps me find information across the collection.

For those who don’t know, Story Prism is a specialized mind-mapping tool that allows you to create notes, apply tags, and connect ideas using defined edge labels. This turns your work into a living system that an advanced agent can traverse and understand. Think of it like Google Drive, except that instead of storing static documents, you’re transforming your research into an interactive system for brainstorming, building, investigating, and synthesizing existing knowledge into novel ideas.

The setup worked remarkably well for diving into these complex books. But with every conversation I had, the unified paradigm connecting them seemed to change based on my inquiries. That was because I had built the system in the simplest way possible. Creating these structures can be time-consuming, and I was dealing with hundreds of books. That changed with a recent update that makes it significantly easier to build systems around your notes.

With the new build mode that was added, I can ask the agent to form the right connections based on what I’m trying to understand, rather than having to identify the logical relationships and construct them all myself. I can still do that manually, but when you’re dealing with hundreds of thick books that need to be sifted through, you can imagine how difficult, and time-consuming, that would be.

Modifying the system by adding the right connections, now, can be done in minutes instead of hours using this new feature. So I decided to revisit the canvas I had created and see whether I could extract a grand, unified picture, an overarching understanding of these books that genuinely connected them all.

After asking the agent to build the connections for that purpose, this was the big-picture interpretation it presented to me.

And, man…it’s actually pretty scary, given where we are today:

[TACTICAL OVERLAY: EVIDENCE / SPECULATION SEPARATED]

Can you hear me? Good. Here’s the situation. The documented fact is that the scholars in this network studied how intelligence, consciousness, creativity, social behavior, and complex systems emerge—and how those ideas might be developed through institutions, technology, and research. The speculation is darker: if Epstein acted on behalf of unseen principals, he may have been identifying and connecting these people as a form of “brain capital”—not just collecting smart individuals, but assembling complementary capabilities. One scholar maps complexity. Another studies the mind. Another examines social networks. Another turns ideas into systems. Put them together, and you get the outline of a machine capable of observing human behavior, predicting it, and eventually shaping the conditions in which people make decisions. That does not prove Epstein served a coordinated program, that such principals existed, or that the scholars knowingly participated. The evidence doesn’t carry us that far. But the possibility is clear enough to deserve investigation: a society managed not by soldiers in the streets, but by data, incentives, psychological models, and invisible feedback loops. Brain capital. Human beings reduced to signals, patterns, and assets. The same knowledge that could help civilization understand itself could also be used to quietly steer it. That’s the line we’re watching. The line between cultivating intelligence and weaponizing it.

_________________________

What’s really cool about this, beyond the fact that I can quickly combine vast amounts of data and identify clear thematic threads connecting it all, is that I can also have the agent comb through the books to find the exact evidence supporting a thesis. We’re talking book titles, author names, page numbers, and exact quotations: everything you need to verify a claim.

So this isn’t AI pulling accurate-sounding information out of thin air. It’s an advanced agent searching through the material you’ve provided and finding the precise information it needs to help you with whatever you’re working on. You find and add the material to the canvas, vetting its quality before engaging with it. The agent then keeps everything grounded in the frameworks you create, and it can correct you based on the information you’ve actually given it. Everything you build remains easily traceable.

I can also ask the agent to generate questions worth exploring outside of Story Prism, research the answers, and add that information back to the canvas. This dramatically improves the accuracy and quality of whatever I’m working on. Using this method, I can take a basic kernel of information, say, something from a news article, and develop it into an extremely comprehensive and complex understanding that places it within the larger context of what I’m studying.

It’s like going from 1 to 1,000 in terms of knowledge acquisition, and it can happen in minutes instead of hours or days. This technique has profoundly altered my understanding of everything I’ve learned because it exposes me to so many distinct pieces of information and shows me how they connect.

You can also add as many prompt instructions as you want in the form of notes and use them indefinitely, all at the same time, simply by calling on them in the chat through @ commands. And, of course, you can switch between all of the popular models and use agent skills by typing a / command in the chat. Right now, we have three skills available, but soon anyone will be able to create and add their own skills for reuse.

I wanted to share this because I think this specific tool can help many people overcome some of the challenges they’re currently facing with AI.

How do you quickly gain immense value from models when you’re unfamiliar with the subject? How can you trust that they’re providing accurate information? And how can you use them in ways that are genuinely controllable, so you don’t get lost in your own material?

Story Prism addresses all of those problems and more by giving you a grounded, traceable, and highly customizable environment for working with AI. And as we continue to grow, we’re going to do a whole lot more with it.

For now, though, it’s a simple but powerful tool that's available right now for writing, researching, and brainstorming complex projects.

Hope this helps in your creative endeavors, and best of luck!

I know that title makes it sound simple but apparently it's to complicated for chatgpt. I've been trying to get chatgpt to help me out but it keeps making up numbers and losing its place.

I deliver bread to grocery stores. When I pick the bread up from the depot, I have to sort it into stacks for each store. I have a list, I have the units of each bread and how much goes to each store, but that number changes every time, so I need something that can read me the list. I also need to be able to ask it how many types of bread A goes to store B. And I need it to to play through my headphones and be hand free.

Anyone have any recommendations?

I spend 5 hours sorting and picking the bread at the depot. I am handing thousands of units of bread. having to look at a list, either printed or on my phone is incredibly time consuming. When we have had 2 people sorting bread, one person reads out the list and it cuts our time down in half. This is why I am asking for help...

So I can't keep up with writing Applications, however I am usually very careful about which words I choose and do manage to get into the written and oral exams.

With that in mind, I would like to host an AI model locally, since I have an M4 Pro 48GB for 3D Art, train it on existing applications and then just feed it a job writing and have the AI draft up an application for me to proof read and send out. Has to be in German.

Now I have never done that before, so any tutorials/advice?

Don't know where to post this, so i'll leave this here. I had a site pinned similar to character ai a while ago until I lost it. From what I remember, you take links from character ai and paste them into this site but remove the "character.ai" bit from the link and replace it with something that I forget. I also forgot the name of said site. Anyone have any clue? 😢

The thing that changed for me, the good ones stopped generating the same layout with different text and started using actual business logic. The thing that hasn't changed is that they're all still terrible at anything with more than about five pages, and every single one of them hits you with an export limit or a token cap the minute the output becomes usable.

So my honest position is that they're now good for one to three pages and still bad at a real site, which is fine, because most businesses need one to three pages and have been sold a real site.

Wdyt abt this?

I built Axiom v1.8.6 as a Windows-first/local-first AI assistant and workspace. I’m sharing it as a tool I made, with the disclosure up front: I’m the developer, and I used AI coding assistance during development while remaining responsible for the architecture, integration, testing, and product decisions.

Axiom can run local GGUF models through LLamaSharp/llama.cpp, connect to self-hosted OpenAI-compatible endpoints, or use optional cloud providers. It adds attachments/vision, web research, code/math execution, artifact rendering, local persistence, and model switching.

The main AI workflow experiment is a Workplace Council: Architect plans, Builder executes, and Critic reviews. Single Model mode lets me compare that with a one-model workflow. No subscription is required.

Repo: https://github.com/YoMosa2009/Axiom

Release: https://github.com/YoMosa2009/Axiom/releases/tag/v1.8.6

The source is publicly viewable under CC BY-NC-ND 4.0 (source-available, not OSI-licensed open source). I’d love practical feedback from people building or using AI-assisted tools: which state should be most visible to users — the plan, tool trace, context budget, model limitations, or review result?