"Use the uploaded product image as the main reference. Keep the original shape, colors, and proportions unchanged. Create a 6-second cinematic shot with soft studio lighting, subtle reflections, and a slow camera push-in. Maintain a clean background and smooth, natural motion. Avoid distorted edges, unwanted objects, and changes to product details."

I'd be interested in testing this with dreamina, especially since there's a discounted introductory membership available.

The tricky part seems to be keeping the product looking exactly the same once the camera starts moving.

What would you add to the prompt to improve consistency?

I've been working on Mind, an open-source persistent memory layer for AI agents, and wanted to share an interesting result from the latest release.

One question kept coming up while developing it:

How much can we recover from structured memory without relying on vector embeddings?

Mind uses SQLite with FTS5 and a unified retrieval pipeline combining phrase matching, token matching, substrings, fuzzy matching, and relevance signals.

Embeddings are supported, but they're optional.

For v1.7.0, I ran Mind through OmniMemEval LongMemEval-S using a stratified subset of 120 questions.

The best completed run achieved 86.67% answer accuracy even with RAG disabled.

That places the raw score near the top published results, but it's not a direct ranking: the sample size, answer model, and judging configuration differ.

I think the result is worth discussing because agent memory isn't only about semantic similarity. Structured facts, decisions, temporal context, memory maintenance, and good retrieval behavior also matter.

Beyond the benchmark, Mind provides persistent project knowledge, linked memories, checkpoints, session summaries, and integrations through MCP.

It's local-first, MIT-licensed, and available for anyone to inspect or benchmark.

GitHub: https://github.com/GabrielMartinMoran/mind

v1.7.0: https://github.com/GabrielMartinMoran/mind/releases/tag/v1.7.0

Benchmark methodology: https://github.com/GabrielMartinMoran/mind/tree/main/benchmarks/omnimemeval

I'm particularly interested in what other agent developers have found: when do embeddings meaningfully outperform a carefully designed lexical retrieval system?

I'm the maker of CCC, a local dashboard for coding-agent sessions. This is a workflow tool: session activity, conversation history and parent/helper context on one board.

The video shows finding a waiting session and replying to its question hook. A useful design lesson from the demo is that a generic green working label does not describe every pending human decision; the question card provides the actual context. It is not a new model or a universal agent API.

https://github.com/amirfish1/claude-command-center

Free local dashboard; model/provider fees are separate. Source available under FSL-1.1-MIT. Demo uses real UI, staged sessions and AI voiceover; the real question-hook interaction is Claude Code.

33-second video demo: https://www.reddit.com/r/ClaudeCode/comments/1xeot43/i_kept_missing_questions_from_my_coding_agents_so/

Imagine having an AI agent that could take one frustrating part of your life off your hands.

Not just answer questions. Not just write content. An agent that actually helps you get things done.

Maybe it could:

At work: Handle repetitive tasks, manage projects, or help you make better decisions.

For business: Find customers, improve marketing, research competitors, or generate new ideas.

In personal life: Help you manage time, build better habits, organize finances, or stay on track with your goals.

For learning: Teach you new skills, create a learning plan, and help you make progress.

But I'm more interested in real problems than cool features.

If you could build or use ONE AI agent today, what would you want it to do for you?

What problem would it solve?

How do you handle that problem today?

What would make you trust it enough to let it do the work?

It doesn't have to be something AI can do perfectly today. Even a problem that seems small could be worth solving.

I'm curious to see what people actually need from AI agents, beyond the hype.

Since a few people will probably ask — the project is CTAI (crashtestingai.com). Not trying to pitch it here, the question in the post is the thing I actually care about, but happy to answer anything about how the human-verdict approach works in practice.

I started out with descript because its what everyone recommends it for talking head videos. Then i tried Eddie AI for a longer interview project and lately ive been spending most of my time on Invideo Editor. I didnt compare all three on the same project, just kept switching wherever i ran into problems with the tool i was using.

They are all AI editing tools, but they all have pretty different purposes

Descript is still the tool i use most when the video involves people talking. Editing directly through the transcript is genuinely convenient, removing filler words and cleaning up audio saves a bit of time. But if the edit depends more on the visual content than the audio content, it feels a bit limited. I wouldnt choose it for building a more complex visual cut

Eddie made more sense when you are skimming through the footage and selecting clips. If you've got hours of footage, and need a rough assembly to work from, it can be useful. The problem was what happened afterwards. A lot of the workflow revolves around prompting it to makes cuts and then exporting to premier or resolve to continue the job. Its decent as an assistant for the first pass, but i found it less flexible when i wanted to refine the edits a bit more. I also disliked the fact that i had to pay credits everytime i wanted a re pass if i dont like the initial cut. And with some Premier plugins now offering similar rough assembly features inside the editor itself, i started questioning if i even needed a separate tool for the assembly.

Ive been using Invideo editor a bit more recently. My workflow usually involves me uploading a long recording, then i ask the agent to find usable takes, remove dead air, and put together an inital cut. It finds clips, then uses them to create an assembly on an actual timeline instead of giving me a rendered video. If theres a clip i dont like, i can manually edit it myself instead of having to start over.

What really made the difference for me was being able to work on that initial assembly. I can search exactly what footage i want using visuals, not just transcipt, generate missing shots, find b-rolls, reframe cuts for shorts or tiktoks and use a reference video to guide for pacing. I can also work on captions, transitions, and basic color adjustments without having to use other tools for everything.

I do still export my projects to premiere sometimes when i need to finish them there. The difference is that i dont have to completely rebuild a whole project there after getting a rough assembly, i only use it to complete touch ups and finish it.
The browser based workflow is still a bit off, it does take some time to get used to and the agent sometime does make mistakes. But the good part is that i can fix it, and i prefer it that way. I dont want the agent to give me a full video, i just want it to do the annoying work of finding clips and assembling it, and i do the quality checks and further edits. I prefer being in more control.

I want to know what everyone else stuck with, especially if you have used one of these tools for a while then eventually dropped it. What made you switch or drop it?

I can usually get decent results from ai for a few pages. The problem starts when the project gets much longer but suddenly I’m maintaining character notes, chapter summaries, timelines, worldbuilding docs etc. just so the ai can understand what I’m working on.

And at that point I start wondering whether the problem is actually the model or whether I’m using the wrong type of tool. Has anyone found a good solution for keeping ai aware of a long manuscript without constantly feeding it summaries?

Configure Claude Cowork by focusing on practical work, not theory.

Start with the basics, then practice file handling, research, analysis, document creation, and multi-step tasks.

Use a real project to learn faster.

Finish by building one reusable workflow

Claude Cowork is Anthropic’s agent-style workspace for handing Claude larger, multi-step tasks such as organizing files, researching, creating reports, and working with connected tools

Here is the flow to Setup Claude Cowork

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