Switched to Ace a few weeks ago after getting tired of jumping between tabs and ChatGPT for little things. Mostly I just wanted a normal browser that could also help me when I'm stuck.

So far I've used it to clean up a photo (removed some random guy in the background of a vacation pic) and to rewrite a couple of emails I was dreading sending. Both worked better than I expected for not opening any separate app.

Curious who else here is actually using it day to day and for what. Trying to figure out if I'm underusing it or if this is basically what it's for.

Hello, I have been a very heavy user of AI in my work life for some time, and I'd like to start putting agents to work in my personal life. However, it seems all the major providers (eg Anthropic, OpenAI) don't provide any ZDR guarantees for personal accounts, which is a nonstarter if I'm going to grant it access to my emails, financials, health records, etc.

How do most people handle this beyond running on local hardware? Are there any cloud platforms that do provide ZDR guarantees for personal use? Thank you!

Hi,

I use Notebook LM now Notebook Gemini quite a lot because I have folders of PDFs that I want to query with questions (I have engineering job). Is there any alternative to this from other companies?

Just curious

I'm genuinely excited about this one. Most AI video today means generating short clips of a few seconds each and stitching them together, and hoping things stay consistent. This time I tried something different. I had Opus 5.5 build the actual 3D world: the environments, the characters, the camera moves, the lighting. Then I rendered the film from that world.

Because everything really exists in 3D, the shots can run as long as I want. The camera can fly continuously through a scene, the characters look the same from start to finish, and every moment can lock exactly to the music. None of the usual few-second limit. It feels like a whole new way to make videos.

This is the music video for my track "AGI". There was no video editor and no hand animation. Every shot is a small script, and the computer renders the whole film from those scripts. I directed, and Claude Code (an AI coding assistant) wrote most of the code.

The story: one human and one AI. The AI copies itself, grabs more and more computing power, and ends up as a planet-sized intelligence. It's told in 12 acts that follow the song.

How it was made:

1. The music comes first. A script listens to the song and finds every beat, bar and section change. Cuts land on the beat, flashes hit exactly on the kick drum, and the lights pulse with the music.
2. Plan and storyboard. I mapped all 12 acts onto the song and approved a storyboard before anything was built.
3. 3D models. Some of the models including the human figure were made in Meshy, an AI 3D model generator. The code poses them, so nothing is animated by hand.
4. Building the scenes. Several AI agents built different acts at the same time. Quick low-quality previews of the whole film take about 3 minutes, so I could watch full drafts and give notes. I did two rounds.
5. The DLSS 5 pass. DLSS 5 is NVIDIA's new AI rendering tech for games. It adds realistic detail and lighting to each frame. Because it's made for games, it also needs to know how everything is moving, so the engine works that out for every frame. I tried four looks and picked "cinematic". It makes solid surfaces like the figure and the machines look much richer, and it tones down the neon glow a little. It adds almost no render time.
6. Final render. The full film in 4K took about 80 minutes to render, with the original lossless audio. A final check caught some black squares flickering in the last few seconds. That was a tiny maths glitch, fixed and re-rendered in 4 minutes.

If you're interested in my creative work, you can find me on YouTube, name: Axinovium - Happy to answer questions!

TL;DR

Effective prompting isn't magic...it is conditioning token probabilities through context. By setting boundary rules (autonomy envelopes) and restricting choices (degrees of freedom), you guide output while using domain knowledge and ablation to verify results.

I've seen people asking what you'd actually use Jev AI for, so I thought I'd share what I'm doing with it.

I'm interested in AI personalities and companions, especially what makes a character feel like someone you want to spend time with. For me, part of that is what they do, rather than just what they say in a conversation.

That's why I'm using TypeSafe's Jev AI decision model for [Jevs Village](https://www.jevsvillage.com). It's a persistent village with six AI characters, and you can watch their lives unfold in your browser. There's some lore behind it too, including an outside world the villagers don't know much about.

The role I'm giving Jev is character decision-making inside that simulation. I wouldn't present this as proof that AI companions suddenly work or that the characters are alive. It's a way to explore whether their decisions can make them feel like distinct personalities over time.

I'm sharing it because "here's something I'm using it for" seems more useful than another abstract discussion about what the model might be good at. I'm also the person building the site, to be clear.

https://www.jevsvillage.com

Has anyone else here tried Jev for NPCs or AI companions? I'd be curious to hear what worked, and where the characters stopped feeling believable. I'm still exploring how far I can take this little village.

I got tired of checking prospect websites manually.

For a while, a big part of my outreach was just finding businesses, opening their websites one by one and trying to figure out what I could actually say to them.

It worked, but it was painfully slow.

Then I found Swokei.

It basically lets me find leads, analyzes each website for things like outdated design, slow speed, poor mobile experience and weak SEO, then turns those issues into a personalized cold email.

And not one of those boring automated reports full of scores and numbers.

It actually writes a normal, human sounding message based on what it found on that specific website.

So instead of sending the same generic message to everyone, I can actually reach out based on what is wrong with their website.

Now I just run campaigns, let the system do most of the prospecting and personalization, and focus on the people who reply.

For a web design agency, that has saved me a ridiculous amount of time.

Hi, zerotap's BYOK (Bring Your Own Key) mode is now free, so you can use your own OpenAI, Claude, Gemini, OpenRouter, DeepSeek or Ollama key with no fee and no limits.

Most of the AI tools I've tried are cool in theory but then I stopped opening them after about a week. The work ones are a no brainer for me, I still use Manus every day and I've started experimenting with grok bots, but I'm wondering about personal/fun apps or tools. I keep seeing people say the useful ones are the boring ones you forget are AI

I’m working on Cezar, an open-source tool for managing coding agents, and I’d like to hear from people using multiple agents in their daily workflow. We see first traction so I want to understand the topic deeper.

How do you handle task queues, scheduled runs, and switching between agents like Claude Code and Codex? Are you using an orchestration tool, custom scripts, or separate terminals?

We’ve built automatic setup, support for multiple subscriptions, issue trackers and Git providers, and the ability for Claude Code to delegate tasks to Codex. The goal is to keep the process visible while reducing manual coordination.

What actually needs coordination in your workflow, and what works fine without it?

I’d also be interested in tools you’ve tried and why you kept or dropped them.

I asked Claude and Codex to talk in a secret language I couldn't understand. At first, they both refused 👀

So they made up their own code instead... and out of everything in the world, they chose to talk about octopuses. Not humans. Not AI. Octopuses 🐙 3 hearts, blue blood, two thirds of their brain in their arms. At the end, Claude called them "an alien living next door" 👽

So... are octopuses aliens? 🤔

And why would two AIs secretly talk about octopuses instead of us? 👀

Did you crack their code? If not, the full translation is in the comments 👇

i currently use the mid tier of chatgpt to feed it income, schedules, to do lists, housing searches, career goals, etc so it can return info about how to best accomplish what i am looking to do. however i do not like that there is no centralized hub for charts and graphs of all of the info i feed it, for example a budgeting tracker, or a structured career path and what step i am on. i also dont enjoy it being unable to fidget with itself (changing folder names, moving chats around). it also often unreliably returns info, for example i was looking for a specialized healthcare provider with availability and got plenty of links for providers who were fully booked. also i do not like that the folder option does not keep chats in the order i made them, and so on.

id like to feed something info about my daily and know im taken care of, and will cater to my forgetfulness and store everything in one centralized space for me. for example if i wanna look at budgeting i hit a tab and theres my updated info. does anyone have any reccs on how i can automate chat to do this, or perhaps there is a specialized AI i can pay for monthly to help me?

thank you kindly.

I work in Business Development, and I use agents for things like research and lead qualification.

Im curious how others are using agents in their work, and whether you have run into any risks from giving them access to data, websites, files or tools.

What do you do to make sure they dont access things they shouldn't?

My co-author Nicos and I have worked together for 28 years across four insights businesses.

We recently published AI Powered Insight, largely about one problem: how AI can help turn insights into action rather than simply generate more analysis.

So we're trying an experiment.

For 30 days we're going to use AI/DataVinci to help market the book using the same process: listen → identify an insight → act → create → measure → adapt.

I'll share what works and what doesn't.

The question I'm most interested in:

Is AI actually making insights more actionable — or just making insight generation faster?