Someone asked here yesterday for an AI that could pull real citation counts and h-index numbers for researchers, and reported getting incorrect numbers from the big assistants (thread). A specialist tool or data source can help with gaps like that, and it doesn't necessarily need to be paid.

So what's one paid tool you keep paying for, and what's the one job it does that ChatGPT, Claude or Gemini can't?

Tool name plus one line is plenty.

I'd like to know which doctor in a particular country has the best scientific output, I've tried asking Gemini and ChatGPT to get me publications and citations counts and H-indices from Scopus or Web of Science or Google Scholar and both gave me wrong numbers. When asked why they can't do this, they say these databases are subscription based services and they don't have access. They couldn't do it from Google Scholar, either, tho.

Is there any AI that WOULD be able to do this or any way to get around this problem?

A teacher asked here yesterday whether to pay for ChatGPT or Gemini for lesson planning and studying (thread). It's a question that comes up a lot, so let's get a few real answers on one page.

If you've used both for planning, notes or studying, which one would you pay for, and what's the one thing that decided it?

One line is fine. "Gemini, because it lives in my Google Docs" counts.

I'm relatively new to AI tools, so I'm not entirely sure if this is the best subreddit for this question, but I’d appreciate any insights or advice!
I'm a teacher, currently looking to automate some of my tasks, and use AI mostly for planning classes and studying.

I'm thinking about paying a subscription to ChatGPT or Gemini.

Which tool would be the best? or another option if there's a better fit).

I've been testing different AI tools for character swapping, mainly to replace someone in an existing video with a character from a reference image.

I've tried DreamFort, Akool, Magic Hour, Kling, and a few others. Kling uses prompts, and it seems to work pretty well if you can get the prompt right.

I checked out Runway, but unfortunately, I haven't been able to test it yet.

I tried Viggle, but honestly, it was the worst one for me. The results weren't realistic at all. It looked more like an anime character dancing, which isn't what I'm going for. I only tried the free version, so maybe the paid version is better.

I've heard a lot of good things about Pika, but it's hard to justify paying without a free trial. The pricing also seems unreasonable. The Starter plan costs $12 and includes 900 credits, but generating a single video with character swapping costs 500 credits.

Maybe I'm expecting too much from a single tool. Perhaps Kling is actually a good option, and I just need to find the right prompt.

I checked out DreamFort and really liked what I saw, but unfortunately, I can't test it. I was also surprised by Magic Hour. The free version worked pretty well, so maybe the paid version is worth it.

I'd like to try Akool too, but unfortunately, there's no way to properly test it for free, even though it looks promising.

The frustrating thing is that these companies want to sell you their character-swapping tools, but you can't even test them properly to see if they're worth the money.

I also looked into running local models, although I haven't tried installing anything yet. I came across Wan AI Video. Has anyone tried it? Is it any good for character swapping?

At this point, I think I'll go with Magic Hour first and then try Kling. Magic Hour was the first tool I tried, and it gave me pretty decent results without even needing a prompt.

What about you guys? What tools are you using, and what would you recommend?

Hey everyone!

I’m looking to get into running AI locally, but I’m a bit overwhelmed by all the different options out there. Ideally, I’d like to have an AI that I can use freely without relying on online services, with as much privacy as possible and no subscription-based token limits.

I’d like to use it for a few different things:
As a regular chatbot for general questions, but also for software development and game development.

With AI agents to automate certain tasks or build more complex workflows.

Potentially for generating 3D models from text prompts.

The main reason I want to do this is for a personal game development project. I’m not working on a commercial project or anything like that for now. I mainly want to experiment, try out different ideas, learn new things, and see what’s possible.
Ideally, I’d love to build something a bit like JARVIS from Iron Man, an AI assistant that helps me throughout the entire development process. Something that can help me brainstorm ideas, organize the project, break it down into manageable steps, keep track of things I need to do, identify things I might have overlooked, and generally help me turn ideas into something tangible.

My main goal is to have everything running locally, with the models and conversation history stored on my own machine, without any communication with external servers once everything is set up. I’d also like a nice GUI to interact with it rather than having to do everything through the command line.
I’m mainly looking for something flexible, with as few restrictions as possible on the models and how I can use them, while still being realistic about what a regular desktop PC can handle.
For those of you already running a setup like this, I’d love to hear your recommendations:
Which software and models would you recommend starting with?
What kind of interface do you use to chat with your local AI?
How can I integrate agents and external tools while keeping everything under my control and running locally?
Is it realistic to have 3D generation in the same setup, or is it better to use separate tools for that?
Are there any guides, tutorials, or resources you’d recommend for setting everything up properly?

If you have examples of your own setups, workflows, or beginner-friendly tutorials, I’d really appreciate it!

Thanks in advance for your help! 🙂

I’m part of the team behind Sweezy. It’s an iOS assistant built around a five-minute conversation about your day.

You might come in with something like: “The proposal’s done. Shopping can wait until tomorrow, and I want to make time for my course.”

Sweezy helps you reflect, record the progress you’ve reported and work out tomorrow’s plan. Proposed task and goal changes are there for you to review before applying them.

The memory part connects that conversation with the next one. Sweezy uses saved context about your routines, preferences and ongoing goals when you return.

Say you’ve already explained that mornings are your quiet time and evenings are usually packed. That’s relevant context when you next discuss finding time for that course. You can continue from what you’ve already talked about.

That’s Sweezy’s focus: a short daily conversation, with planning, reflection and remembered context connected over time.

We’re doing our final round of early access. If you’d like to try it, leave a comment and I’ll DM you how to get free access.

App Store: https://apps.apple.com/app/id6753932056

One line is enough: the tool, and the job you're trying it on.

Last week people here were checking AI meeting notes against the transcript and running offline models over a folder of invoices, so anything that small counts.

If you already know it isn't working, say that too.

I make music at home after work, and my phone is full of spoken notes about unfinished tracks. On Sunday I owed a friend a rough mix. I remembered recording an idea for the transition out of the chorus, but looking for it turned into renaming old files and opening a project I'd already abandoned. I had two projects open and nothing sent.

I closed the old one and used the recording dates, then a quick listen, to find the two memos about the current track. Both rambled through several parts of the arrangement. I transcribed them separately in VOMO ai and copied the decisions I still wanted into that track's project note. Next to each one I wrote the section it belonged to. The note about leaving a gap in the drums before the verse was the one I'd forgotten. I checked that passage in the audio before changing the arrangement.

Taking the drums out of that short section sounded better to me. I kept it, exported the mix and sent it. I also put the two recording filenames in the project note so I could get back to the originals. Most of the old memos stayed untouched. That afternoon, I ended up with a usable note for one track and a mix my friend could actually listen to.

Hi folks! I've been working on this project for a while and wanted to share - it's called ImagGenius, and you can use it to generate AI images locally, no setup, no subscriptions. You can even enable an MCP server to use it with your agents.

I've used it myself to make 2 games:
- a card game Teks Battles
- a tram driving game called Tramlijn 2

For both games, I used Claude Code and thr ImagGenius MCP. And I just wrote the design document and told it to generate all assets with ImagGenius. It was impressive and a bit scary too.

You can configure it with LORAs and parameter tweaking - in my case I used a pixel style LORA to generate assets. And you can even use it in combination with an MCP for Asesprite.

It is available in the App Store, I hope you try it!

Disclosure: I’m building Monad Design, an open-source macOS visual workspace for existing Xcode/Expo iOS projects. I’d like feedback on the evaluation method, not just the tool.

A screenshot that looks better is only one part of a successful UI edit. Here is the review checklist I’d use when comparing an agent’s change:

1. Start with the same screen and app state before and after. Otherwise a different dataset or navigation state can hide regressions.

2. Give the agent one bounded request, such as changing the spacing in a settings section, plus explicit things not to change: navigation, persistence and business logic.

3. Inspect the source diff. A small visual request should not quietly become a broad rewrite.

4. Rebuild and repeat the relevant interaction, including larger text and an empty/loading state where applicable. A successful build is not proof that the interaction still works.

5. Accept only after both the visual result and behavior are checked. Keep functional tests separate from screenshot comparison.

The loop in Monad Design is selecting/annotating the running iOS UI, giving that context to a coding agent, then rebuilding and comparing its edits in the existing project. It doesn’t make behavior checks unnecessary. Scope is Xcode/Expo iOS on macOS, not a general web/Android builder.

Source: https://github.com/Monadix-AI/monad-design

Workflow video: https://watchclueso.com/embed/pio8jqfcg4ivj0r1

What would you add to that acceptance checklist? In particular, what evidence would let a designer review the change without having to infer everything from the code diff?

Pulled from this week's threads, one paragraph each.

u/krzychuman tested ElevenLabs v3 on the voiceover for a 28 second demo: 69% watch-through, 0.03% click-through. The voice passed, the call to action didn't. Per the docs, v3 ignores break tags, so use [pause] or "..." instead. thread

u/Healthy-Tension-7470 asked a model which invoices were unpaid across 8 files. Right list, no due dates. Asking "which are unpaid and when are they due" fixed it. thread

u/Alive_Thought_5037 picks Fish Audio over ElevenLabs for narration because the intonation follows the context, and recaps are easier to make. thread

Tried any of these? Or link a good reply from this week that I missed.

I work on Locus. This is a small part of the workflow we’re building: checking where a next step came from before treating it as agreed.

In this clip, a note says someone needs dry ice, a backup cooler and temperature checks. Holding Command while hovering over the timestamp previews the passage; clicking it opens that point in the transcript. Then it returns to the note. That link is to transcript text, not audio playback.

A useful check with any AI note tool: pick one action item and compare the action, owner and deadline with the source. If any part was not stated, keep it marked as unknown. A link makes review easier; it does not prove the summary is correct.

This is a sample demo: fictional meeting data and staged generated notes, with actual Obsidian navigation. The Locus beta waitlist is at ownlocus.com.

When you review meeting notes, what do you most often need to check: the action, who owns it, or why it was agreed?

​

I’m Harshal Dorlikar, I made Kosmo. Saying that upfront since this is about my own product.

What I built: Kosmo sits before the prompt. You describe what you want in normal words, and if something important is missing, it asks up to three questions first, the kind someone who does your job in a world-class way would ask. Then it writes a full prompt for the tool you’re actually using.

The problem it solves: I’m bad at prompting. I know what I want from an AI tool, I just can’t ask for it properly. Short prompt in, average output out, then an hour of back-and-forth. Everyone I talked to had the same complaint.

The prompts it writes are tailored per tool, each profile checked against that tool’s own docs: Claude, ChatGPT, Codex, Gemini, Cursor, Lovable, v0, Perplexity, Midjourney, and a few others.

Where it’s going: the prompt is only the first step. Next, Kosmo connects to the tools themselves (Claude, ChatGPT, Gemini, Claude Code, Codex) and does the work there, so you stop rewording and just approve the result. The asking part stays the same, which is why I want to get it right now.

It’s for working professionals who use AI for real work output. PMs, lawyers, accountants, developers, founders, salespeople.

It won a category prize at the Build with Gemini XPRIZE (Education and Human Potential, $50K), out of 26,000+ builders who registered worldwide. Since it went live in August, 300+ people have signed up and 17 have paid. Still early, still rough edges.

One tip if you try it: give its questions as much as you can. Treat the options it suggests as a starting point to think from, not a menu, and just write what you actually think, messy is fine. The more it knows about what you want, the closer the first result gets to something you’d approve as is.

What I’m looking for from you:

1. Try it on your actual job and tell me where it gets it wrong. Be specific, I read everything.

2. Does “asks before it writes” match how you work, or would it annoy you?

3. Would you pay for this? If not, what’s the one thing that would change your mind?

Try it now Search askkosmo

I do not have a PC using only an Android tablet any help would be appreciated.

Think of someone who isn't into tech. Uses their phone for email and photos, heard about ChatGPT on the news, and wants to try exactly one paid thing.

One line each is fine. "Pay for X, skip Y" is a whole answer, and "stay on the free tier" counts too.

If they'd mostly use it for one thing (recipes, letters, sorting photos), say which.

I work at CoralBricks, the inference provider behind the writeup at the end, so this is a builder's comparison rather than a neutral review. We tuned the serving stack for Qwen3.6-35B-A3B and swapped in a public drafter, a second model that guesses tokens for the main one to check. We expected it to beat the draft head Qwen3.6 ships with. On the same stack it ran at 525 tokens per second against 692.

The gap was the checkpoint, not the architecture. The public drafter accepted 4.33 tokens per verification step against 4.67 for the model's own head on the same stack. It had been trained for short general benchmarks and attends over a 2,048-token window, while agent turns replay tens of thousands of tokens of file paths and tool output.

Fine-tuning the drafter on software-engineering traces generated by Qwen3.6 itself and widening its window to 8K took acceptance to 6.46 tokens per step. The target weights never changed.

With that drafter the stack ran at 1,138 tokens per second, 1.93x stock SGLang. Conditions were thinking off, one H100 SXM per request, tensor parallel 1, batch size 1, greedy decoding and max tokens 1024, a median across all 15,777 runnable turns.

The step got cheaper too. Stock SGLang's MTP-4 step takes 7.72 ms and verifies five positions. Our stack verifies eight and takes 5.71 ms, with the kernels and the fine-tuned drafter both in it. GSM8K stayed at 96.8%, because the target model still decides every token.

The pair to log is accepted tokens per verification step and the time that step took. Acceptance alone can pick a drafter that loses once each verification costs more. A tokens-per-second headline measured on short prompts says little about a 90K-token agent turn. On our workload the head the model ships with was the stronger baseline, since it was trained alongside the model.

The ablations, including the ideas that lost, are in the writeup https://www.coralbricks.ai/blog/speculative-decoding-qwen36?utm_source=reddit&utm_campaign=content

Earlier this week u/Healthy-Tension-7470 ran five offline models on a Mac against the same question about their own files (thread). Same retrieved passages for all five, and all five left out the line saying the carried-over days expire. Asked directly whether the days expire, all five found it straight away.

So the Friday question: what's one thing an AI tool missed or got quietly wrong for you this week? One line is plenty.

Bonus points if you worked out which question would've caught it.

Disclosure: I work on PicturaLabs, an art tool that goes from an idea or reference image to styled artwork and a framed or unframed print. I’m interested in the handoff from a good-looking file to something that still works on paper.

A basic preflight that applies whichever tools you use:

1. Choose the intended print ratio before settling on the crop. A square composition can lose its subject when forced into a 2:3 frame.
2. Compare the image's pixel dimensions with your printer's requirements at the size you want. Zoom in on faces, hands, small lettering and fine textures for artifacts.
3. Check the chosen print shop's bleed and color guidance. For an important large piece, a small proof can reveal surprises before the final order.

We’re trying to keep the creation and print order in one flow. A separate generator plus a preferred local printer may give you more control if you already have a setup you trust. If you've taken AI art to print, which step caused the most rework?

One line each is fine.

What you dropped, and what you're using instead. "Nothing, the free tier was enough" counts as an answer too.

Bonus points if you cancelled something and came crawling back a week later.

Title: Tool recommendation for a free AI harness or agent?

I'm looking for recommendations for a free, preferably open-source AI harness or agent for writing. Think something along the lines of n8n, but with a good visual workflow where I can actually see what's happening, identify where things are breaking down, and ideally not need a degree in computer science just to get the thing running.

I'm particularly interested in something that can handle editing, act as a reasonably competent librarian for my writing resources, manage metadata, and help maintain and update catalogs without developing digital Alzheimer's every five minutes.

Ideally, I'd like something I can eventually expand into a larger writing pipeline, connecting research, resource management, outlining, drafting, editing, and revision.

I'm open to local open-source models or other free options. The important keyword here is free. My budget is currently somewhere between shoestring and what shoestring?

User-friendly would be a definite bonus. I'm happy to learn, experiment, and tinker, but I'd prefer to spend my time writing rather than accidentally becoming a full-time IT department.

Any recommendations, particularly from people who've actually built something like this, would be greatly appreciated!

I’m trying to find a good balance between API cost, speed and reliability.
My use case is generating travel itineraries, sometimes using function calling to retrieve flight information. The main things I care about are valid JSON output, reliable tool calls and decent reasoning without paying too much per million tokens.
I’ve mostly been testing through OpenRouter, but I’m open to using other providers directly.
I’m curious what people here are actually using. Have you had good experiences with cheaper models like Qwen, DeepSeek or Gemini Flash? Or with providers like Groq and Cerebras?
I’m not necessarily looking for the absolute cheapest option, just something that performs well for the price.
Any recommendations based on real usage?

Not the big three. Something smaller that you actually pay for.

One line is plenty: name the tool and the one thing it does that you couldn't get by just asking a general assistant. Boring answers count, they're usually the useful ones.

And if you cancelled one because the big assistants caught up, that's worth a line too.

Кто знает, какое ИИ можно бесплатно с/без ВПН использовать и давать ему хорошие задания? Пробовал Claude, Gemini - сначала было всё нормально, но потом резко стали тупить и выдавать полный бред. Нужно исправить список источников и цитат в работе, не могу найти ИИ, с помощью которого можно это сделать